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Validation of Egalitarian Education Questionnaire using Rasch Measurement Model

Rakami NMHN, Ismail NAH, Abu Kassim NL, Idrus F

J Appl Meas, 2020;21(1):91-100.
PMID: 32129771

This paper describes the process of assessing the unidimensionality and validity of egalitarian education (EE) items based on the Rasch measurement model. Egalitarian education was measured by a self-developed 5 EE items of Likert-scale format. The process of assessing the validity of EE items involved a collection of data from 400 Malay teachers, who are teaching in government school around peninsular of Malaysia where the measurement of construct validity for the overall EE items were established using Winsteps. Various Rasch measurement tools were utilized to demonstrate the true unidimensionality and validity measure of the EE items and in meeting the needs of the Rasch measurement model. The findings show that the validity and unidimensionality of EE items can be truly established and can satisfy the characteristics of the Rasch measurement model.

Matched MeSH terms: Logistic Models*
Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification

Algamal ZY, Lee MH

Comput Biol Med, 2015 Dec 1;67:136-45.
PMID: 26520484 DOI: 10.1016/j.compbiomed.2015.10.008

Cancer classification and gene selection in high-dimensional data have been popular research topics in genetics and molecular biology. Recently, adaptive regularized logistic regression using the elastic net regularization, which is called the adaptive elastic net, has been successfully applied in high-dimensional cancer classification to tackle both estimating the gene coefficients and performing gene selection simultaneously. The adaptive elastic net originally used elastic net estimates as the initial weight, however, using this weight may not be preferable for certain reasons: First, the elastic net estimator is biased in selecting genes. Second, it does not perform well when the pairwise correlations between variables are not high. Adjusted adaptive regularized logistic regression (AAElastic) is proposed to address these issues and encourage grouping effects simultaneously. The real data results indicate that AAElastic is significantly consistent in selecting genes compared to the other three competitor regularization methods. Additionally, the classification performance of AAElastic is comparable to the adaptive elastic net and better than other regularization methods. Thus, we can conclude that AAElastic is a reliable adaptive regularized logistic regression method in the field of high-dimensional cancer classification.

Matched MeSH terms: Logistic Models
Fulltext The performance of classical and robust logistic regression estimators in the presence of outliers

Habshah, M., Syaiba, B.A.

Pertanika Journal of Science & Technology, 2012;20(2):313-325.
MyJurnal

It is now evident that the estimation of logistic regression parameters, using Maximum LikelihoodEstimator (MLE), suffers a huge drawback in the presence of outliers. An alternative approach is touse robust logistic regression estimators, such as Mallows type leverage dependent weights estimator(MALLOWS), Conditionally Unbiased Bounded Influence Function estimator (CUBIF), Bianco andYohai estimator (BY), and Weighted Bianco and Yohai estimator (WBY). This paper investigates therobustness of the preceding robust estimators by using real data sets and Monte Carlo simulations. Theresults indicate that the MLE behaves poorly in the presence of outliers. On the other hand, the WBYestimator is more efficient than the other existing robust estimators. Thus, it is suggested that the WBYestimator be employed when outliers are present in the data to obtain a reliable estimate.

Matched MeSH terms: Logistic Models
Performance of Euler-Maruyama, 2-Stage SRK and 4-Stage SRK in Approximating the Strong Solution of Stochastic Model

Norhayati Rosli, Arifah Bahar, Yeak SH, Haliza Abdul Rahman, Madihah Md. Salleh

Sains Malaysiana, 2010;39.

Stochastic differential equations play a prominent role in many application areas including finance, biology and epidemiology. By incorporating random elements to ordinary differential equation system, a system of stochastic differential equations (SDEs) arises. This leads to a more complex insight of the physical phenomena than their deterministic counterpart. However, most of the SDEs do not have an analytical solution where numerical method is the best way to resolve this problem. Recently, much work had been done in applying numerical methods for solving SDEs. A very general class of Stochastic Runge-Kutta, (SRK) had been studied and 2-stage SRK with order convergence of 1.0 and 4-stage SRK with order convergence of 1.5 were discussed. In this study, we compared the performance of Euler-Maruyama, 2-stage SRK and 4-stage SRK in approximating the strong solutions of stochastic logistic model which describe the cell growth of C. acetobutylicum P262. The MS-stability functions of these schemes were calculated and regions of MS-stability are given. We also perform the comparison for the performance of these methods based on their global errors.

Matched MeSH terms: Logistic Models
Fulltext Predictors and Outcomes of Neurological Deterioration in Intracerebral Hemorrhage: Results from the TICH-2 Randomized Controlled Trial

Law ZK, Dineen R, England TJ, Cala L, Mistri AK, Appleton JP, et al.

Transl Stroke Res, 2021 Apr;12(2):275-283.
PMID: 32902808 DOI: 10.1007/s12975-020-00845-6

Neurological deterioration is common after intracerebral hemorrhage (ICH). We aimed to identify the predictors and effects of neurological deterioration and whether tranexamic acid reduced the risk of neurological deterioration. Data from the Tranexamic acid in IntraCerebral Hemorrhage-2 (TICH-2) randomized controlled trial were analyzed. Neurological deterioration was defined as an increase in National Institutes of Health Stroke Scale (NIHSS) of ≥ 4 or a decline in Glasgow Coma Scale of ≥ 2. Neurological deterioration was considered to be early if it started ≤ 48 h and late if commenced between 48 h and 7 days after onset. Logistic regression was used to identify predictors and effects of neurological deterioration and the effect of tranexamic acid on neurological deterioration. Of 2325 patients, 735 (31.7%) had neurological deterioration: 590 (80.3%) occurred early and 145 (19.7%) late. Predictors of early neurological deterioration included recruitment from the UK, previous ICH, higher admission systolic blood pressure, higher NIHSS, shorter onset-to-CT time, larger baseline hematoma, intraventricular hemorrhage, subarachnoid extension and antiplatelet therapy. Older age, male sex, higher NIHSS, previous ICH and larger baseline hematoma predicted late neurological deterioration. Neurological deterioration was independently associated with a modified Rankin Scale of > 3 (aOR 4.98, 3.70-6.70; p

Matched MeSH terms: Logistic Models
Fulltext Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Nhu VH, Shirzadi A, Shahabi H, Singh SK, Al-Ansari N, Clague JJ, et al.

Int J Environ Res Public Health, 2020 04 16;17(8).
PMID: 32316191 DOI: 10.3390/ijerph17082749

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms-Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine-in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

Matched MeSH terms: Logistic Models*
Fractal characterization of bpn weights evolution

Gunasekaran S, Venkatesh B, Sagar BS

Int J Neural Syst, 2004 Apr;14(2):139-45.
PMID: 15112371

Training methodology of the Back Propagation Network (BPN) is well documented. One aspect of BPN that requires investigation is whether or not the BPN would get trained for a given training data set and architecture. In this paper the behavior of the BPN is analyzed during its training phase considering convergent and divergent training data sets. Evolution of the weights during the training phase was monitored for the purpose of analysis. The evolution of weights was plotted as return map and was characterized by means of fractal dimension. This fractal dimensional analysis of the weight evolution trajectories is used to provide a new insight to understand the behavior of BPN and dynamics in the evolution of weights.

Matched MeSH terms: Logistic Models
Nonclinical Features in Predictive Modeling of Cardiovascular Diseases: A Machine Learning Approach

Sajid MR, Muhammad N, Zakaria R, Shahbaz A, Bukhari SAC, Kadry S, et al.

Interdiscip Sci, 2021 Jun;13(2):201-211.
PMID: 33675528 DOI: 10.1007/s12539-021-00423-w

BACKGROUND: In the broader healthcare domain, the prediction bears more value than an explanation considering the cost of delays in its services. There are various risk prediction models for cardiovascular diseases (CVDs) in the literature for early risk assessment. However, the substantial increase in CVDs-related mortality is challenging global health systems, especially in developing countries. This situation allows researchers to improve CVDs prediction models using new features and risk computing methods. This study aims to assess nonclinical features that can be easily available in any healthcare systems, in predicting CVDs using advanced and flexible machine learning (ML) algorithms.
METHODS: A gender-matched case-control study was conducted in the largest public sector cardiac hospital of Pakistan, and the data of 460 subjects were collected. The dataset comprised of eight nonclinical features. Four supervised ML algorithms were used to train and test the models to predict the CVDs status by considering traditional logistic regression (LR) as the baseline model. The models were validated through the train-test split (70:30) and tenfold cross-validation approaches.
RESULTS: Random forest (RF), a nonlinear ML algorithm, performed better than other ML algorithms and LR. The area under the curve (AUC) of RF was 0.851 and 0.853 in the train-test split and tenfold cross-validation approach, respectively. The nonclinical features yielded an admissible accuracy (minimum 71%) through the LR and ML models, exhibiting its predictive capability in risk estimation.
CONCLUSION: The satisfactory performance of nonclinical features reveals that these features and flexible computational methodologies can reinforce the existing risk prediction models for better healthcare services.

Matched MeSH terms: Logistic Models
Continuous medication monitoring: A clinical model to predict adherence to medications among chronic kidney disease patients

Islahudin F, Lee FY, Tengku Abd Kadir TNI, Abdullah MZ, Makmor-Bakry M

Res Social Adm Pharm, 2021 10;17(10):1831-1840.
PMID: 33589374 DOI: 10.1016/j.sapharm.2021.02.002

BACKGROUND: An adherence model is required to optimise medication management among chronic kidney disease (CKD) patients, as current assessment methods overestimate the true adherence of CKD patients with complex regimens. An approach to assess adherence to individual medications is required to assist pharmacists in addressing non-adherence.
OBJECTIVE: To develop an adherence prediction model for CKD patients.
METHODS: This multi-centre, cross-sectional study was conducted in 10 tertiary hospitals in Malaysia using simple random sampling of CKD patients with ≥1 medication (sample size = 1012). A questionnaire-based collection of patient characteristics, adherence (defined as ≥80% consumption of each medication for the past one month), and knowledge of each medication (dose, frequency, indication, and administration) was performed. Continuous data were converted to categorical data, based on the median values, and then stratified and analysed. An adherence prediction model was developed through multiple logistic regression in the development group (n = 677) and validated on the remaining one-third of the sample (n = 335). Beta-coefficient values were then used to determine adherence scores (ranging from 0 to 7) based on the predictors identified, with lower scores indicating poorer medication adherence.
RESULTS: Most of the 1012 patients had poor medication adherence (n = 715, 70.6%) and half had good medication knowledge (n = 506, 50%). Multiple logistic regression analysis determined 4 significant predictors of adherence: ≤7 medications (constructed score = 2, p

Matched MeSH terms: Logistic Models
Fulltext The influence of socio-demographic factors and product attributes on attitudes toward purchasing special rice among Malaysian consumers

International Food Research Journal, 2011;18(3):1135-1142.
MyJurnal

The study investigated socio-demographic factors and product attributes affecting purchase decision of special rice by Malaysian consumer. The primary data were analyzed by using binary logit model.
Demographic factors and consumer preference for special rice (with reference to basmati rice) attributes were identified to affect purchasing behavior for special rice. Size of household, marital status, number of children, household income and gender of consumers are the main socio-demographic factors that significantly influence households’ choices of special rice for home consumption in the Klang Valley area. The findings also suggest that product attributes such as flavor and aroma, availability, brand name and quality also influence the frequent purchasing of Basmati rice among the Malaysian consumers. However price and easy preparation are not significant in influencing the frequent purchasing of Basmati rice since most consumers are aware that special rice such as Basmati is expensive and all rice has to be prepared in a usual way.

Matched MeSH terms: Logistic Models
Fulltext An Intelligent Proposed Model for Task Offloading in Fog-Cloud Collaboration Using Logistics Regression

Bukhari MM, Ghazal TM, Abbas S, Khan MA, Farooq U, Wahbah H, et al.

Comput Intell Neurosci, 2022;2022:3606068.
PMID: 35126487 DOI: 10.1155/2022/3606068

Smart applications and intelligent systems are being developed that are self-reliant, adaptive, and knowledge-based in nature. Emergency and disaster management, aerospace, healthcare, IoT, and mobile applications, among them, revolutionize the world of computing. Applications with a large number of growing devices have transformed the current design of centralized cloud impractical. Despite the use of 5G technology, delay-sensitive applications and cloud cannot go parallel due to exceeding threshold values of certain parameters like latency, bandwidth, response time, etc. Middleware proves to be a better solution to cope up with these issues while satisfying the high requirements task offloading standards. Fog computing is recommended middleware in this research article in view of the fact that it provides the services to the edge of the network; delay-sensitive applications can be entertained effectively. On the contrary, fog nodes contain a limited set of resources that may not process all tasks, especially of computation-intensive applications. Additionally, fog is not the replacement of the cloud, rather supplement to the cloud, both behave like counterparts and offer their services correspondingly to compliance the task needs but fog computing has relatively closer proximity to the devices comparatively cloud. The problem arises when a decision needs to take what is to be offloaded: data, computation, or application, and more specifically where to offload: either fog or cloud and how much to offload. Fog-cloud collaboration is stochastic in terms of task-related attributes like task size, duration, arrival rate, and required resources. Dynamic task offloading becomes crucial in order to utilize the resources at fog and cloud to improve QoS. Since this formation of task offloading policy is a bit complex in nature, this problem is addressed in the research article and proposes an intelligent task offloading model. Simulation results demonstrate the authenticity of the proposed logistic regression model acquiring 86% accuracy compared to other algorithms and confidence in the predictive task offloading policy by making sure process consistency and reliability.

Matched MeSH terms: Logistic Models
Fulltext Predicting Return to Work after Cardiac Rehabilitation using Machine Learning Models

Yuan CJ, Varathan KD, Suhaimi A, Ling LW

J Rehabil Med, 2023 Jan 09;55:jrm00348.
PMID: 36306152 DOI: 10.2340/jrm.v54.2432

OBJECTIVE: To explore machine learning models for predicting return to work after cardiac rehabilitation.
SUBJECTS: Patients who were admitted to the University of Malaya Medical Centre due to cardiac events.
METHODS: Eight different machine learning models were evaluated. The models included 3 different sets of features: full features; significant features from multiple logistic regression; and features selected from recursive feature extraction technique. The performance of the prediction models with each set of features was compared.
RESULTS: The AdaBoost model with the top 20 features obtained the highest performance score of 92.4% (area under the curve; AUC) compared with other prediction models.
CONCLUSION: The findings showed the potential of using machine learning models to predict return to work after cardiac rehabilitation.

Matched MeSH terms: Logistic Models
Factors associated with the functional balance among diabetes mellitus patients in Padang, Indonesia

Desnita R, Surya DO

Med J Malaysia, 2023 Jul;78(4):523-525.
PMID: 37518926

INTRODUCTION: One of the common problems in patients with diabetes mellitus is a decrease in balance stability. A decrease in balance stability will result in functional limitations, an increased risk of falling and injury and a decrease in patient productivity. This study aimed to analyse the factors associated with functional balance in diabetes mellitus patients in Padang, Indonesia.
MATERIALS AND METHODS: This research design is crosssectional. The number of samples in this study was 132 diabetes mellitus patients. Chi-square test and binary logistic regression were used to examine the factors associated with functional balance in diabates mellitus patients.
RESULTS: Factors associated with functional balance in diabetes mellitus patients were age.
CONCLUSION: This study highlights that age, gender and degree of neuropathy are significant factors associated with functional balance in diabetes mellitus patients. Nurses must enhance health education about prevention and risk factors that affect functional balance in diabetes mellitus patients.

Matched MeSH terms: Logistic Models
Fulltext Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis

Ab Rasid AM, Muazu Musa R, Abdul Majeed APP, Musawi Maliki ABH, Abdullah MR, Mohd Razmaan MA, et al.

PLoS One, 2024;19(2):e0296467.
PMID: 38329954 DOI: 10.1371/journal.pone.0296467

The identification and prediction of athletic talent are pivotal in the development of successful sporting careers. Traditional subjective assessment methods have proven unreliable due to their inherent subjectivity, prompting the rise of data-driven techniques favoured for their objectivity. This evolution in statistical analysis facilitates the extraction of pertinent athlete information, enabling the recognition of their potential for excellence in their respective sporting careers. In the current study, we applied a logistic regression-based machine learning pipeline (LR) to identify potential skateboarding athletes from a combination of fitness and motor skills performance variables. Forty-five skateboarders recruited from a variety of skateboarding parks were evaluated on various skateboarding tricks while their fitness and motor skills abilities that consist of stork stance test, dynamic balance, sit ups, plank test, standing broad jump, as well as vertical jump, were evaluated. The performances of the skateboarders were clustered and the LR model was developed to classify the classes of the skateboarders. The cluster analysis identified two groups of skateboarders: high and low potential skateboarders. The LR model achieved 90% of mean accuracy specifying excellent prediction of the skateboarder classes. Further sensitivity analysis revealed that static and dynamic balance, lower body strength, and endurance were the most important factors that contributed to the model's performance. These factors are therefore essential for successful performance in skateboarding. The application of machine learning in talent prediction can greatly assist coaches and other relevant stakeholders in making informed decisions regarding athlete performance.

Matched MeSH terms: Logistic Models
Fulltext Pacifier Use and its Association with Breastfeeding and Acute Respiratory Infection (ARI) in Children Below 2 Years Old

Mahmud SZ, Joanita S, Khairun Nisa J, Balkish MN, Tahir A

Med J Malaysia, 2013 Apr;68(2):125-8.
PMID: 23629557 MyJurnal

Extensive literature reviews showed that pacifier usage is associated with early cessation of breast feeding, as well as respiratory infection. This cross sectional study was a part of the bigger study of The Third National Health Morbidity Survey conducted throughout Malaysia in 2006. Survival and pearson cox regression was done to find association between pacifier user and breast feeding duration. Logistic Regression was done to find association between variables of interest. The prevalence of pacifier use was 32.9%. Chinese children reported significantly higher usage of pacifier (95% CI; 47.5, 58.7) as well as those resided in urban area (95% CI;32.5,37.7). One third of pacifier user had stopped breastfeeding at 6 months of age. Those with pacifier users were significantly shorter in breast feeding duration and significantly associated with non exclusivity in breastfeeding. Those without pacifier user were significantly associated with ever breast fed.(p value=0.001). There was no significant association between pacifier use with acute respiratory infection. Factors such as ethnicity and residential are non modifiable whereas modifiable factor such as pacifier use is certainly needed to be addressed at maternal and child health care level.
Study name: National Health and Morbidity Survey (NHMS-2006)

Matched MeSH terms: Logistic Models
Fulltext Risk factors associated with low birth weight infants in the Malaysian population

Boo NY, Lim SM, Koh KT, Lau KF, Ravindran J

Med J Malaysia, 2008 Oct;63(4):306-10.
PMID: 19385490 MyJurnal

This study aimed to identify the risk factors which were significantly associated with low birth weight (LBW, <2500 g) infants among the Malaysian population. This was a case-control study carried out at the Tuanku Jaafar Hospital, Seremban, Malaysia over a five-month period. Cases were all infants born with birth weight less than 2500 g. Control infant were selected with the help a random sampling table from among infants with birth weight of > or =2500 g born on the same day in the hospital. Of 3341 livebirths delivered in the hospital, 422 (12.6%) were LBW infants. Logistic regression analysis showed that, after controlling for various potential confounders, the only significant risk factors associated with infants of LBW were gestational age (adjusted odds ratio (OR)=0.6, 95% C.I.: 0.5, 0.6; < 0.0001), maternal pre-pregnancy weight (adjusted OR = 0.97, 95% C.I.: 0.95, 0.99; p < 0.0001), nulliparity (adjusted OR = 3.4, 95% C.I.: 2.2, 5.1; p < 0.0001), previous history of LBW infants (adjusted OR = 2.3, 95% C.I.: 1.4, 3.8; p=0.001) and PIH during current pregnancy (adjusted OR=3.3, 95% C.I.: 1.6, 6.6; p = 0.001). A number of potentially preventable or treatable risk factors were identified to be associated with LBW infants in Malaysia.

Matched MeSH terms: Logistic Models
Menstrual and Reproductive Factors, Hormone Use, and Risk of Pancreatic Cancer: Analysis From the International Pancreatic Cancer Case-Control Consortium (PanC4)

Lujan-Barroso L, Zhang W, Olson SH, Gao YT, Yu H, Baghurst PA, et al.

Pancreas, 2016 11;45(10):1401-1410.
PMID: 27088489

OBJECTIVES: We aimed to evaluate the relation between menstrual and reproductive factors, exogenous hormones, and risk of pancreatic cancer (PC).
METHODS: Eleven case-control studies within the International Pancreatic Cancer Case-control Consortium took part in the present study, including in total 2838 case and 4748 control women. Pooled estimates of odds ratios (ORs) and their 95% confidence intervals (CIs) were calculated using a 2-step logistic regression model and adjusting for relevant covariates.
RESULTS: An inverse OR was observed in women who reported having had hysterectomy (ORyesvs.no, 0.78; 95% CI, 0.67-0.91), remaining significant in postmenopausal women and never-smoking women, adjusted for potential PC confounders. A mutually adjusted model with the joint effect for hormone replacement therapy (HRT) and hysterectomy showed significant inverse associations with PC in women who reported having had hysterectomy with HRT use (OR, 0.64; 95% CI, 0.48-0.84).
CONCLUSIONS: Our large pooled analysis suggests that women who have had a hysterectomy may have reduced risk of PC. However, we cannot rule out that the reduced risk could be due to factors or indications for having had a hysterectomy. Further investigation of risk according to HRT use and reason for hysterectomy may be necessary.

Matched MeSH terms: Logistic Models
Fulltext Assessment of potential drug-drug interactions and its associated factors in the hospitalized cardiac patients

Murtaza G, Khan MY, Azhar S, Khan SA, Khan TM

Saudi Pharm J, 2016 Mar;24(2):220-5.
PMID: 27013915 DOI: 10.1016/j.jsps.2015.03.009

Drug-drug interactions (DDIs) may result in the alteration of therapeutic response. Sometimes they may increase the untoward effects of many drugs. Hospitalized cardiac patients need more attention regarding drug-drug interactions due to complexity of their disease and therapeutic regimen. This research was performed to find out types, prevalence and association between various predictors of potential drug-drug interactions (pDDIs) in the Department of Cardiology and to report common interactions. This study was performed in the hospitalized cardiac patients at Ayub Teaching Hospital, Abbottabad, Pakistan. Patient charts of 2342 patients were assessed for pDDIs using Micromedex® Drug Information. Logistic regression was applied to find predictors of pDDIs. The main outcome measure in the study was the association of the potential drug-drug interactions with various factors such as age, gender, polypharmacy, and hospital stay of the patients. We identified 53 interacting-combinations that were present in total 5109 pDDIs with median number of 02 pDDIs per patient. Overall, 91.6% patients had at least one pDDI; 86.3% were having at least one major pDDI, and 84.5% patients had at least one moderate pDDI. Among 5109 identified pDDIs, most were of moderate (55%) or major severity (45%); established (24.2%), theoretical (18.8%) or probable (57%) type of scientific evidence. Top 10 common pDDIs included 3 major and 7 moderate interactions. Results obtained by multivariate logistic regression revealed a significant association of the occurrence of pDDIs in patient with age of 60 years or more (p

Matched MeSH terms: Logistic Models
Fulltext SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications

Karim A, Salleh R, Khan MK

PLoS One, 2016;11(3):e0150077.
PMID: 26978523 DOI: 10.1371/journal.pone.0150077

Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks' back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies.

Matched MeSH terms: Logistic Models
Falls and Fear of Falling After Stroke: A Case-Control Study

Goh HT, Nadarajah M, Hamzah NB, Varadan P, Tan MP

PM R, 2016 12;8(12):1173-1180.
PMID: 27268565 DOI: 10.1016/j.pmrj.2016.05.012

BACKGROUND: Falls are common after stroke, with potentially serious consequences. Few investigations have included age-matched control participants to directly compare fall characteristics between older adults with and without stroke. Further, fear of falling, a significant psychological consequence of falls, has only been examined to a limited degree as a risk factor for future falls in a stroke population.
OBJECTIVE: To compare the fall history between older adults with and without a previous stroke and to identify the determinants of falls and fear of falling in older stroke survivors.
DESIGN: Case-control observational study.
SETTING: Primary teaching hospital.
PARTICIPANTS: Seventy-five patients with stroke (mean age ± standard deviation, 66 ± 7 years) and 50 age-matched control participants with no previous stroke were tested.
METHODS: Fall history, fear of falling, and physical, cognitive, and psychological function were assessed. A χ2 test was performed to compare characteristics between groups, and logistic regression was performed to determine the risk factors for falls and fear of falling.
MAIN OUTCOME MEASURES: Fall events in the past 12 months, Fall Efficacy Scale-International, Berg Balance Scale, Functional Ambulation Category, Fatigue Severity Scale, Montreal Cognitive Assessment, and Patient Healthy Questionnaire-9 were measured for all participants. Fugl-Meyer Motor Assessment was used to quantify severity of stroke motor impairments.
RESULTS: Twenty-three patients and 13 control participants reported at least one fall in the past 12 months (P = .58). Nine participants with stroke had recurrent falls (≥2 falls) compared with none of the control participants (P < .01). Participants with stroke reported greater concern for falling than did nonstroke control participants (P < .01). Female gender was associated with falls in the nonstroke group, whereas falls in the stroke group were not significantly associated with any measured outcomes. Fear of falling in the stroke group was associated with functional ambulation level and balance. Functional ambulation level alone explained 22% of variance in fear of falling in the stroke group.
CONCLUSIONS: Compared with persons without a stroke, patients with stroke were significantly more likely to experience recurrent falls and fear of falling. Falls in patients with stroke were not explained by any of the outcome measures used, whereas fear of falling was predicted by functional ambulation level. This study has identified potentially modifiable risk factors with which to devise future prevention strategies for falls in patients with stroke.
LEVEL OF EVIDENCE: III.

Matched MeSH terms: Logistic Models

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