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A decision tree model for the prediction of homodimer folding mechanism

Suresh A, Karthikraja V, Lulu S, Kangueane U, Kangueane P

Bioinformation, 2009 Nov 17;4(5):197-205.
PMID: 20461159

The formation of protein homodimer complexes for molecular catalysis and regulation is fascinating. The homodimer formation through 2S (2 state), 3SMI (3 state with monomer intermediate) and 3SDI (3 state with dimer intermediate) folding mechanism is known for 47 homodimer structures. Our dataset of forty-seven homodimers consists of twenty-eight 2S, twelve 3SMI and seven 3SDI. The dataset is characterized using monomer length, interface area and interface/total (I/T) residue ratio. It is found that 2S are often small in size with large I/T ratio and 3SDI are frequently large in size with small I/T ratio. Nonetheless, 3SMI have a mixture of these features. Hence, we used these parameters to develop a decision tree model. The decision tree model produced positive predictive values (PPV) of 72% for 2S, 58% for 3SMI and 57% for 3SDI in cross validation. Thus, the method finds application in assigning homodimers with folding mechanism.

Matched MeSH terms: Decision Trees
Fulltext Personality prediction based on social media using decision tree algorithm

Tan Lee CY, Ngatirin NR, Zainol Z

Pertanika Journal of Science & Technology, 2017;25(104):237-248.
MyJurnal

Personality represents the mixture of features and qualities that built an individual’s distinctive characters including thinking, feeling and behaving. Traditionally, self-assessment method via questionnaire is the most common means to identify personality. Since recommender systems and advertisement
campaigns have evolved rapidly, personality computing has become a popular research field to provide personalisation to users. Currently, researchers have utilised social media data for automatically predicting personality. However, it is complex to mine the social media data as they are noisy, free-format, and
of varying length and multimedia. This paper proposes a decision tree C4.5 algorithm to automatically predict personality based on Big Five model. The Big Five Inventory and ZeroR algorithm were included to be served as the baseline for performance evaluation. Experimental evaluation demonstrated that C4.5
performs better than ZeroR in terms of accuracy.
Keywords: Big Five, decision tree, personality, social media

Matched MeSH terms: Decision Trees
Fulltext A hybrid interpretable deep structure based on adaptive neuro-fuzzy inference system, decision tree, and K-means for intrusion detection

Liu J, Yinchai W, Siong TC, Li X, Zhao L, Wei F

Sci Rep, 2022 Dec 01;12(1):20770.
PMID: 36456582 DOI: 10.1038/s41598-022-23765-x

For generating an interpretable deep architecture for identifying deep intrusion patterns, this study proposes an approach that combines ANFIS (Adaptive Network-based Fuzzy Inference System) and DT (Decision Tree) for interpreting the deep pattern of intrusion detection. Meanwhile, for improving the efficiency of training and predicting, Pearson Correlation analysis, standard deviation, and a new adaptive K-means are used to select attributes and make fuzzy interval decisions. The proposed algorithm was trained, validated, and tested on the NSL-KDD (National security lab-knowledge discovery and data mining) dataset. Using 22 attributes that highly related to the target, the performance of the proposed method achieves a 99.86% detection rate and 0.14% false alarm rate on the KDDTrain+ dataset, a 77.46% detection rate on the KDDTest+ dataset, which is better than many classifiers. Besides, the interpretable model can help us demonstrate the complex and overlapped pattern of intrusions and analyze the pattern of various intrusions.

Matched MeSH terms: Decision Trees
Utilizing decision tree machine model to map dental students' preferred learning styles with suitable instructional strategies

Shoaib LA, Safii SH, Idris N, Hussin R, Sazali MAH

BMC Med Educ, 2024 Jan 11;24(1):58.
PMID: 38212703 DOI: 10.1186/s12909-023-05022-5

BACKGROUND: Growing demand for student-centered learning (SCL) has been observed in higher education settings including dentistry. However, application of SCL in dental education is limited. Hence, this study aimed to facilitate SCL application in dentistry utilising a decision tree machine learning (ML) technique to map dental students' preferred learning styles (LS) with suitable instructional strategies (IS) as a promising approach to develop an IS recommender tool for dental students.
METHODS: A total of 255 dental students in Universiti Malaya completed the modified Index of Learning Styles (m-ILS) questionnaire containing 44 items which classified them into their respective LS. The collected data, referred to as dataset, was used in a decision tree supervised learning to automate the mapping of students' learning styles with the most suitable IS. The accuracy of the ML-empowered IS recommender tool was then evaluated.
RESULTS: The application of a decision tree model in the automation process of the mapping between LS (input) and IS (target output) was able to instantly generate the list of suitable instructional strategies for each dental student. The IS recommender tool demonstrated perfect precision and recall for overall model accuracy, suggesting a good sensitivity and specificity in mapping LS with IS.
CONCLUSION: The decision tree ML empowered IS recommender tool was proven to be accurate at matching dental students' learning styles with the relevant instructional strategies. This tool provides a workable path to planning student-centered lessons or modules that potentially will enhance the learning experience of the students.

Matched MeSH terms: Decision Trees
A hybrid method for classifying cognitive states from fMRI data

Parida S, Dehuri S, Cho SB, Cacha LA, Poznanski RR

J Integr Neurosci, 2015 Sep;14(3):355-68.
PMID: 26455882 DOI: 10.1142/S0219635215500223

Functional magnetic resonance imaging (fMRI) makes it possible to detect brain activities in order to elucidate cognitive-states. The complex nature of fMRI data requires under-standing of the analyses applied to produce possible avenues for developing models of cognitive state classification and improving brain activity prediction. While many models of classification task of fMRI data analysis have been developed, in this paper, we present a novel hybrid technique through combining the best attributes of genetic algorithms (GAs) and ensemble decision tree technique that consistently outperforms all other methods which are being used for cognitive-state classification. Specifically, this paper illustrates the combined effort of decision-trees ensemble and GAs for feature selection through an extensive simulation study and discusses the classification performance with respect to fMRI data. We have shown that our proposed method exhibits significant reduction of the number of features with clear edge classification accuracy over ensemble of decision-trees.

Matched MeSH terms: Decision Trees
Fulltext A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique

Nilashi M, Ahmadi H, Shahmoradi L, Ibrahim O, Akbari E

J Infect Public Health, 2018 10 04;12(1):13-20.
PMID: 30293875 DOI: 10.1016/j.jiph.2018.09.009

BACKGROUND: Hepatitis is an inflammation of the liver, most commonly caused by a viral infection. Supervised data mining techniques have been successful in hepatitis disease diagnosis through a set of datasets. Many methods have been developed by the aids of data mining techniques for hepatitis disease diagnosis. The majority of these methods are developed by single learning techniques. In addition, these methods do not support the ensemble learning of the data. Combining the outputs of several predictors can result in improved accuracy in classification problems. This study aims to propose an accurate method for the hepatitis disease diagnosis by taking the advantages of ensemble learning.
METHODS: We use Non-linear Iterative Partial Least Squares to perform the data dimensionality reduction, Self-Organizing Map technique for clustering task and ensembles of Neuro-Fuzzy Inference System for predicting the hepatitis disease. We also use decision trees for the selection of most important features in the experimental dataset. We test our method on a real-world dataset and present our results in comparison with the latest results of previous studies.
RESULTS: The results of our analyses on the dataset demonstrated that our method performance is superior to the Neural Network, ANFIS, K-Nearest Neighbors and Support Vector Machine.
CONCLUSIONS: The method has potential to be used as an intelligent learning system for hepatitis disease diagnosis in the healthcare.

Matched MeSH terms: Decision Trees
Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods

Chen W, Li Y, Xue W, Shahabi H, Li S, Hong H, et al.

Sci Total Environ, 2020 Jan 20;701:134979.
PMID: 31733400 DOI: 10.1016/j.scitotenv.2019.134979

Floods are one of the most devastating types of disasters that cause loss of lives and property worldwide each year. This study aimed to evaluate and compare the prediction capability of the naïve Bayes tree (NBTree), alternating decision tree (ADTree), and random forest (RF) methods for the spatial prediction of flood occurrence in the Quannan area, China. A flood inventory map with 363 flood locations was produced and partitioned into training and validation datasets through random selection with a ratio of 70/30. The spatial flood database was constructed using thirteen flood explanatory factors. The probability certainty factor (PCF) method was used to analyze the correlation between the factors and flood occurrences. Consequently, three flood susceptibility maps were produced using the NBTree, ADTree, and RF methods. Finally, the area under the curve (AUC) and statistical measures were used to validate the flood susceptibility models. The results indicated that the RF method is an efficient and reliable model in flood susceptibility assessment, with the highest AUC values, positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy for the training (0.951, 0.892, 0.941, 0.945, 0.886, and 0.915, respectively) and validation (0.925, 0.851, 0.938, 0.945, 0.835, and 0.890, respectively) datasets.

Matched MeSH terms: Decision Trees
Fulltext Forecasting shear stress parameters in rectangular channels using new soft computing methods

Sheikh Khozani Z, Sheikhi S, Mohtar WHMW, Mosavi A

PLoS One, 2020;15(4):e0229731.
PMID: 32271780 DOI: 10.1371/journal.pone.0229731

Shear stress comprises basic information for predicting the average depth velocity and discharge in channels. With knowledge of the percentage of shear force carried by walls (%SFw) it is possible to more accurately estimate shear stress values. The %SFw, non-dimension wall shear stress ([Formula: see text]) and non-dimension bed shear stress ([Formula: see text]) in smooth rectangular channels were predicted by a three methods, the Bayesian Regularized Neural Network (BRNN), the Radial Basis Function (RBF), and the Modified Structure-Radial Basis Function (MS-RBF). For this aim, eight data series of research experimental results in smooth rectangular channels were used. The results of the new method of MS-RBF were compared with those of a simple RBF and BRNN methods and the best model was selected for modeling each predicted parameters. The MS-RBF model with RMSE of 3.073, 0.0366 and 0.0354 for %SFw, [Formula: see text] and [Formula: see text] respectively, demonstrated better performance than those of the RBF and BRNN models. The results of MS-RBF model were compared with three other proposed equations by researchers for trapezoidal channels and rectangular ducts. The results showed that the MS-RBF model performance in estimating %SFw, [Formula: see text] and [Formula: see text] is superior than those of presented equations by researchers.

Matched MeSH terms: Decision Trees
Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia

Jumin E, Basaruddin FB, Yusoff YBM, Latif SD, Ahmed AN

Environ Sci Pollut Res Int, 2021 Jun;28(21):26571-26583.
PMID: 33484461 DOI: 10.1007/s11356-021-12435-6

Reliable and accurate prediction model capturing the changes in solar radiation is essential in the power generation and renewable carbon-free energy industry. Malaysia has immense potential to develop such an industry due to its location in the equatorial zone and its climatic characteristics with high solar energy resources. However, solar energy accounts for only 2-4.6% of total energy utilization. Recently, in developed countries, various prediction models based on artificial intelligence (AI) techniques have been applied to predict solar radiation. In this study, one of the most recent AI algorithms, namely, boosted decision tree regression (BDTR) model, was applied to predict the changes in solar radiation based on collected data in Malaysia. The proposed model then compared with other conventional regression algorithms, such as linear regression and neural network. Two different normalization techniques (Gaussian normalizer binning normalizer), splitting size, and different input parameters were investigated to enhance the accuracy of the models. Sensitivity analysis and uncertainty analysis were introduced to validate the accuracy of the proposed model. The results revealed that BDTR outperformed other algorithms with a high level of accuracy. The funding of this study could be used as a reliable tool by engineers to improve the renewable energy sector in Malaysia and provide alternative sustainable energy resources.

Matched MeSH terms: Decision Trees
Decision tree model for early use of semi-elemental formula versus standard polymeric formula in critically ill Malaysian patients: A cost-effectiveness study

Adam A, Ibrahim NA, Tah PC, Liu XY, Dainelli L, Foo CY

JPEN J Parenter Enteral Nutr, 2023 Nov;47(8):1003-1010.
PMID: 37497593 DOI: 10.1002/jpen.2554

BACKGROUND: Prevention of enteral feeding interruption (EFI) improves clinical outcomes of critically ill intensive care unit (ICU) patients. This leads to shorter ICU stays and thereby lowers healthcare costs. This study compared the cost of early use of semi-elemental formula (SEF) in ICU vs standard polymeric formula (SPF) under the Ministry of Health (MOH) system in Malaysia.
METHODS: A decision tree model was developed based on literature and expert inputs. An epidemiological projection model was then added to the decision tree to calculate the target population size. The budget impact of adapting the different enteral nutrition (EN) formulas was calculated by multiplying the population size with the costs of the formula and ICU length of stay (LOS). A one-way sensitivity analysis (OWSA) was conducted to examine the effect each input parameter has on the calculated output.
RESULTS: Replacing SPF with SEF would lower ICU cost by MYR 1059 (USD 216) per patient. The additional cost of increased LOS due to EFI was MYR 5460 (USD 1114) per patient. If the MOH replaces SPF with SEF for ICU patients with high EFI risk (estimated 7981 patients in 2022), an annual net cost reduction of MYR 8.4 million (USD 1.7 million) could potentially be realized in the MOH system. The cost-reduction finding of replacing SPF with SEF remained unchanged despite the input uncertainties assessed via OWSA.
CONCLUSION: Early use of SEF in ICU patients with high EFI risk could potentially lower the cost of ICU care for the MOH system in Malaysia.

Matched MeSH terms: Decision Trees
Fulltext Predicting factors for survival of breast cancer patients using machine learning techniques

Ganggayah MD, Taib NA, Har YC, Lio P, Dhillon SK

BMC Med Inform Decis Mak, 2019 03 22;19(1):48.
PMID: 30902088 DOI: 10.1186/s12911-019-0801-4

BACKGROUND: Breast cancer is one of the most common diseases in women worldwide. Many studies have been conducted to predict the survival indicators, however most of these analyses were predominantly performed using basic statistical methods. As an alternative, this study used machine learning techniques to build models for detecting and visualising significant prognostic indicators of breast cancer survival rate.
METHODS: A large hospital-based breast cancer dataset retrieved from the University Malaya Medical Centre, Kuala Lumpur, Malaysia (n = 8066) with diagnosis information between 1993 and 2016 was used in this study. The dataset contained 23 predictor variables and one dependent variable, which referred to the survival status of the patients (alive or dead). In determining the significant prognostic factors of breast cancer survival rate, prediction models were built using decision tree, random forest, neural networks, extreme boost, logistic regression, and support vector machine. Next, the dataset was clustered based on the receptor status of breast cancer patients identified via immunohistochemistry to perform advanced modelling using random forest. Subsequently, the important variables were ranked via variable selection methods in random forest. Finally, decision trees were built and validation was performed using survival analysis.
RESULTS: In terms of both model accuracy and calibration measure, all algorithms produced close outcomes, with the lowest obtained from decision tree (accuracy = 79.8%) and the highest from random forest (accuracy = 82.7%). The important variables identified in this study were cancer stage classification, tumour size, number of total axillary lymph nodes removed, number of positive lymph nodes, types of primary treatment, and methods of diagnosis.
CONCLUSION: Interestingly the various machine learning algorithms used in this study yielded close accuracy hence these methods could be used as alternative predictive tools in the breast cancer survival studies, particularly in the Asian region. The important prognostic factors influencing survival rate of breast cancer identified in this study, which were validated by survival curves, are useful and could be translated into decision support tools in the medical domain.

Matched MeSH terms: Decision Trees*
Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features

Acharya UR, Mookiah MR, Koh JE, Tan JH, Noronha K, Bhandary SV, et al.

Comput Biol Med, 2016 06 01;73:131-40.
PMID: 27107676 DOI: 10.1016/j.compbiomed.2016.04.009

Age-related Macular Degeneration (AMD) affects the central vision of aged people. It can be diagnosed due to the presence of drusen, Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in the fundus images. It is labor intensive and time-consuming for the ophthalmologists to screen these images. An automated digital fundus photography based screening system can overcome these drawbacks. Such a safe, non-contact and cost-effective platform can be used as a screening system for dry AMD. In this paper, we are proposing a novel algorithm using Radon Transform (RT), Discrete Wavelet Transform (DWT) coupled with Locality Sensitive Discriminant Analysis (LSDA) for automated diagnosis of AMD. First the image is subjected to RT followed by DWT. The extracted features are subjected to dimension reduction using LSDA and ranked using t-test. The performance of various supervised classifiers namely Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) are compared to automatically discriminate to normal and AMD classes using ranked LSDA components. The proposed approach is evaluated using private and public datasets such as ARIA and STARE. The highest classification accuracy of 99.49%, 96.89% and 100% are reported for private, ARIA and STARE datasets. Also, AMD index is devised using two LSDA components to distinguish two classes accurately. Hence, this proposed system can be extended for mass AMD screening.

Matched MeSH terms: Decision Trees
Fulltext Risk prediction analysis for classifying type 2 diabetes occurrence using local dataset

M. Hafiz Fazren Abd Rahman, Wan Wardatul Amani Wan Salim, M. Firdaus Abd-Wahab

Biological and Natural Resources Engineering Journal, 2020;3(1):48-61.
MyJurnal

The steep rise of cases pertaining to Diabetes Mellitus (DM) condition among global population has encouraged extensive researches on DM, which led to exhaustive accumulation of data related to DM. In this case, data mining and machine learning applications prove to be a powerful tool in transforming data into meaningful deductions. Several machine learning tools have shown great promise in diabetes classification. However, challenges remain in obtaining an accurate model suitable for real world application. Most disease risk-prediction modelling are found to be specific to a local population. Moreover, real-world data are likely to be complex, incomplete and unorganized, thus, convoluting efforts to develop models around it. This research aims to develop a robust prediction model for classification of type 2 diabetes mellitus (T2DM), with the interest of a Malaysian population, using three different machine learning algorithms; Decision Tree, Support Vector Machine and Naïve Bayes. Data pre-processing methods are utilised to the raw data to improve model performance. This study uses datasets obtained from the IIUM Medical Centre for classification and modelling. Ultimately, the performance of each model is validated, evaluated and compared based on several statistical metrics that measures accuracy, precision, sensitivity and efficiency. This study shows that the random forest model provides the best overall prediction performance in terms of accuracy (0.87), sensitivity (0.9), specificity (0.8), precision (0.9), F1-score (0.9) and AUC value (0.93) (Normal).

Matched MeSH terms: Decision Trees
Fulltext Cost-Effectiveness Analysis of Spending on Research and Development to Address the Needs for Innovative Therapeutic Products in Indonesia

Suwantika AA, Kautsar AP, Zakiyah N, Abdulah R, Boersma C, Postma MJ

Ther Clin Risk Manag, 2020;16:969-977.
PMID: 33116546 DOI: 10.2147/TCRM.S260377

Background: The annual gross domestic expenditure on research and development (GERD) per capita of Indonesia ($24) remains relatively lower than the annual GERD per capita of neighboring countries, such as Vietnam ($36), Singapore ($1804), Malaysia ($361), and Thailand ($111).
Objective: The aim of this study was to conduct a cost-effectiveness analysis of spending on healthcare R&D to address the needs of developing innovative therapeutic products in Indonesia.
Methods: A decision tree model was developed by taking into account four stages of R&D: stage 1 from raw concept to feasibility, stage 2 from feasibility to development, stage 3 from development to early commercialization, and stage 4 from early to full commercialization. Considering a 3-year time horizon, a stage-dependent success rate was applied and analyses were conducted from a business perspective. Two scenarios were compared by assuming the government of Indonesia would increase GERD in health and medical sciences up to 2- and 3-times higher than the baseline (current situation) for the first and second scenario, respectively. Cost per number of innovative products in health and medical sciences was considered as the incremental cost-effectiveness ratio (ICER). Univariate sensitivity analysis was conducted to investigate the effects of different input parameters on the ICER.
Results: There was a statistically significant association (P-value<0.05) between countries' GERD in medical and health sciences with the number of innovative products. We estimated the ICER would be $8.50 million and $2.04 million per innovative product for the first and second scenario, respectively. The sensitivity analysis showed that the success rates in all stages and total GERD were the most influential parameters impacting the ICER.
Conclusion: The result showed that there was an association between GERD in medical and health sciences with the number of innovative products. In addition, the second scenario would be more cost-effective than the first scenario.

Matched MeSH terms: Decision Trees
A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder

Rahman MM, Usman OL, Muniyandi RC, Sahran S, Mohamed S, Razak RA

Brain Sci, 2020 Dec 07;10(12).
PMID: 33297436 DOI: 10.3390/brainsci10120949

Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning's speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.

Matched MeSH terms: Decision Trees
Crash severity along rural mountainous highways in Malaysia: An application of a combined decision tree and logistic regression model

Rusli R, Haque MM, Saifuzzaman M, King M

Traffic Inj Prev, 2018;19(7):741-748.
PMID: 29932734 DOI: 10.1080/15389588.2018.1482537

OBJECTIVE: Traffic crashes along mountainous highways may lead to injuries and fatalities more often than along highways on plain topography; however, research focusing on the injury outcome of such crashes is relatively scant. The objective of this study was to investigate the factors affecting the likelihood that traffic crashes along rural mountainous highways result in injuries.
METHOD: This study proposes a combination of decision tree and logistic regression techniques to model crash severity (injury vs. noninjury), because the combined approach allows the specification of nonlinearities and interactions in addition to main effects. Both a scobit model and a random parameters logit model, respectively accounting for an imbalance response variable and unobserved heterogeneities, are tested and compared. The study data set contains a total of 5 years of crash data (2008-2012) on selected mountainous highways in Malaysia. To enrich the data quality, an extensive field survey was conducted to collect detailed information on horizontal alignment, longitudinal grades, cross-section elements, and roadside features. In addition, weather condition data from the meteorology department were merged using the time stamp and proximity measures in AutoCAD-Geolocation.
RESULTS: The random parameters logit model is found to outperform both the standard logit and scobit models, suggesting the importance of accounting for unobserved heterogeneity in crash severity models. Results suggest that proportion of segment lengths with simple curves, presence of horizontal curves along steep gradients, highway segments with unsealed shoulders, and highway segments with cliffs along both sides are positively associated with injury-producing crashes along rural mountainous highways. Interestingly, crashes during rainy conditions are associated with crashes that are less likely to involve injury. It is also found that the likelihood of injury-producing crashes decreases for rear-end collisions but increases for head-on collisions and crashes involving heavy vehicles. A higher order interaction suggests that single-vehicle crashes involving light and medium-sized vehicles are less severe along straight sections compared to road sections with horizontal curves. One the other hand, crash severity is higher when heavy vehicles are involved in crashes as single vehicles traveling along straight segments of rural mountainous highways.
CONCLUSION: In addition to unobserved heterogeneity, it is important to account for higher order interactions to have a better understanding of factors that influence crash severity. A proper understanding of these factors will help develop targeted countermeasures to improve road safety along rural mountainous highways.

Matched MeSH terms: Decision Trees
Fulltext ECG-based prediction algorithm for imminent malignant ventricular arrhythmias using decision tree

Mandala S, Cai Di T, Sunar MS, Adiwijaya

PLoS One, 2020;15(5):e0231635.
PMID: 32407335 DOI: 10.1371/journal.pone.0231635

Spontaneous prediction of malignant ventricular arrhythmia (MVA) is useful to avoid delay in rescue operations. Recently, researchers have developed several algorithms to predict MVA using various features derived from electrocardiogram (ECG). However, there are several unresolved issues regarding MVA prediction such as the effect of number of ECG features on a prediction remaining unclear, possibility that an alert for occurring MVA may arrive very late and uncertainty in the performance of the algorithm predicting MVA minutes before onset. To overcome the aforementioned problems, this research conducts an in-depth study on the number and types of ECG features that are implemented in a decision tree classifier. In addition, this research also investigates an algorithm's execution time before the occurrence of MVA to minimize delays in warnings for MVA. Lastly, this research aims to study both the sensitivity and specificity of an algorithm to reveal the performance of MVA prediction algorithms from time to time. To strengthen the results of analysis, several classifiers such as support vector machine and naive Bayes are also examined for the purpose of comparison study. There are three phases required to achieve the objectives. The first phase is literature review on existing relevant studies. The second phase deals with design and development of four modules for predicting MVA. Rigorous experiments are performed in the feature selection and classification modules. The results show that eight ECG features with decision tree classifier achieved good prediction performance in terms of execution time and sensitivity. In addition, the results show that the highest percentage for sensitivity and specificity is 95% and 90% respectively, in the fourth 5-minute interval (15.1 minutes-20 minutes) that preceded the onset of an arrhythmia event. Such results imply that the fourth 5-minute interval would be the best time to perform prediction.

Matched MeSH terms: Decision Trees
Fulltext Proposal of a Clinical Decision Tree Algorithm Using Factors Associated with Severe Dengue Infection

Tamibmaniam J, Hussin N, Cheah WK, Ng KS, Muninathan P

PLoS One, 2016;11(8):e0161696.
PMID: 27551776 DOI: 10.1371/journal.pone.0161696

BACKGROUND: WHO's new classification in 2009: dengue with or without warning signs and severe dengue, has necessitated large numbers of admissions to hospitals of dengue patients which in turn has been imposing a huge economical and physical burden on many hospitals around the globe, particularly South East Asia and Malaysia where the disease has seen a rapid surge in numbers in recent years. Lack of a simple tool to differentiate mild from life threatening infection has led to unnecessary hospitalization of dengue patients.

METHODS: We conducted a single-centre, retrospective study involving serologically confirmed dengue fever patients, admitted in a single ward, in Hospital Kuala Lumpur, Malaysia. Data was collected for 4 months from February to May 2014. Socio demography, co-morbidity, days of illness before admission, symptoms, warning signs, vital signs and laboratory result were all recorded. Descriptive statistics was tabulated and simple and multiple logistic regression analysis was done to determine significant risk factors associated with severe dengue.

RESULTS: 657 patients with confirmed dengue were analysed, of which 59 (9.0%) had severe dengue. Overall, the commonest warning sign were vomiting (36.1%) and abdominal pain (32.1%). Previous co-morbid, vomiting, diarrhoea, pleural effusion, low systolic blood pressure, high haematocrit, low albumin and high urea were found as significant risk factors for severe dengue using simple logistic regression. However the significant risk factors for severe dengue with multiple logistic regressions were only vomiting, pleural effusion, and low systolic blood pressure. Using those 3 risk factors, we plotted an algorithm for predicting severe dengue. When compared to the classification of severe dengue based on the WHO criteria, the decision tree algorithm had a sensitivity of 0.81, specificity of 0.54, positive predictive value of 0.16 and negative predictive of 0.96.

CONCLUSION: The decision tree algorithm proposed in this study showed high sensitivity and NPV in predicting patients with severe dengue that may warrant admission. This tool upon further validation study can be used to help clinicians decide on further managing a patient upon first encounter. It also will have a substantial impact on health resources as low risk patients can be managed as outpatients hence reserving the scarce hospital beds and medical resources for other patients in need.

Study site: single ward, in Hospital Kuala Lumpur, Malaysia

Matched MeSH terms: Decision Trees*
Drug use and dosing in chronic kidney disease

Hassan Y, Al-Ramahi R, Abd Aziz N, Ghazali R

Ann Acad Med Singap, 2009 Dec;38(12):1095-103.
PMID: 20052447

One of the most important drug-related problems in patients with chronic kidney disease (CKD) is medication dosing errors. Many medications and their metabolites are eliminated through the kidney. Thus, adequate renal function is important to avoid toxicity. Patients with renal impairment often have alterations in their pharmacokinetic and pharmacodynamic parameters. The clearance of drugs eliminated primarily by renal filtration is decreased by renal disease. Therefore, special consideration should be taken when these drugs are prescribed to patients with impaired renal function. Despite the importance of dosage adjustment in patients with CKD, such adjustments are sometimes ignored. Physicians and pharmacists can work together to accomplish safe drug prescribing. This task can be complex and require a stepwise approach to ensure effectiveness, minimise further damage and prevent drug nephrotoxicity.

Matched MeSH terms: Decision Trees
Optimization of nuclear medicine procedures for the diagnosis and management of thyroid disorders: report of a research coordination meeting held in Vienna, 15-17 December 1986 under the auspices of the International Atomic Energy Agency

Ganatra R, Gembicki M, Nofal M

Nucl Med Commun, 1988 Feb;9(2):131-9.
PMID: 3386976

The third and final meeting of a coordinated research programme on the diagnosis and management of thyroid disorders was held in Vienna from 15 to 17 December 1986. The participants were from Czechoslovakia, Egypt, Israel, Malaysia and Thailand. Each participant had studied between 500 and 1000 patients for thyroid function evaluation by performing T3, T4 and TSH radioimmunoassays. Each had also used the newly available supersensitive immunoradiometric (IRMA) assay in a group of patients to compare the efficiency of the new assay with that of the conventional assay. A microcomputer was provided to each participant for data analysis. Internal quality control was studied by establishing precision profiles and external quality control was on the basis of pooled standard sera in different ranges. Recommendation for the strategy suggested T4 RIA as the test of first choice in each category of thyroid function. IRMA TSH was suggested as a second test in borderline cases.

Matched MeSH terms: Decision Trees

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