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Fulltext Ranking the Meso Level Critical Factors of Electronic Medical Records Adoption Using Fuzzy Topsis Method

Ahmadi H, Nilashi M, Ibrahim O, Raisian K

Curr Health Sci J, 2016 03 29;42(1):82-93.
PMID: 30568817 DOI: 10.12865/CHSJ.42.01.12

As Electronic Medical Records (EMRs) have a great possibility for rising physician's performance in their daily work which improves quality, safety and efficiency in healthcare, they are implemented throughout the world (Boonstra and Broekhuis, 2010). In physician practices the rate of EMRs adoption has been slow and restricted (around 25%) according to Endsley, Baker, Kershner, and Curtin (2005) in spite of the cost savings through lower administrative costs and medical errors related with EMRs systems. The core objective of this research is to identify, categorize, and analyse meso-level factors introduced by Lau et al, 2012, perceived by physicians to the adoption of EMRs in order to give more knowledge in primary care setting. Finding was extracted through questionnaire which distributed to 350 physicians in primary cares in Malaysia to assess their perception towards EMRs adoption. The findings showed that Physicians had positive perception towards some features related to technology adoption success and emphasized EMRs had helpful impact in their office. The fuzzy TOPSIS physician EMRs adoption model in meso-level developed and its factors and sub-factors discussed in this study which provide making sense of EMRs adoption. The related factors based on meso-level perspective prioritized and ranked by using the fuzzy TOPSIS. The purpose of ranking using these approaches is to inspect which factors are more imperative in EMRs adoption among primary care physicians. The result of performing fuzzy TOPSIS is as a novelty method to identify the critical factors which assist healthcare organizations to inspire their users in accepting of new technology.
Organizational decision to adopt hospital information system: an empirical investigation in the case of Malaysian public hospitals

Ahmadi H, Nilashi M, Ibrahim O

Int J Med Inform, 2015 Mar;84(3):166-88.
PMID: 25612792 DOI: 10.1016/j.ijmedinf.2014.12.004

This study mainly integrates the mature Technology-Organization-Environment (TOE) framework and recently developed Human-Organization-Technology (HOT) fit model to identify factors that affect the hospital decision in adopting Hospital Information System (HIS).
Fulltext Accuracy Improvement for Predicting Parkinson's Disease Progression

Nilashi M, Ibrahim O, Ahani A

Sci Rep, 2016 Sep 30;6:34181.
PMID: 27686748 DOI: 10.1038/srep34181

Parkinson's disease (PD) is a member of a larger group of neuromotor diseases marked by the progressive death of dopamineproducing cells in the brain. Providing computational tools for Parkinson disease using a set of data that contains medical information is very desirable for alleviating the symptoms that can help the amount of people who want to discover the risk of disease at an early stage. This paper proposes a new hybrid intelligent system for the prediction of PD progression using noise removal, clustering and prediction methods. Principal Component Analysis (PCA) and Expectation Maximization (EM) are respectively employed to address the multi-collinearity problems in the experimental datasets and clustering the data. We then apply Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for prediction of PD progression. Experimental results on public Parkinson's datasets show that the proposed method remarkably improves the accuracy of prediction of PD progression. The hybrid intelligent system can assist medical practitioners in the healthcare practice for early detection of Parkinson disease.
Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review

Ahmadi H, Gholamzadeh M, Shahmoradi L, Nilashi M, Rashvand P

Comput Methods Programs Biomed, 2018 Jul;161:145-172.
PMID: 29852957 DOI: 10.1016/j.cmpb.2018.04.013

BACKGROUND AND OBJECTIVE: Diagnosis as the initial step of medical practice, is one of the most important parts of complicated clinical decision making which is usually accompanied with the degree of ambiguity and uncertainty. Since uncertainty is the inseparable nature of medicine, fuzzy logic methods have been used as one of the best methods to decrease this ambiguity. Recently, several kinds of literature have been published related to fuzzy logic methods in a wide range of medical aspects in terms of diagnosis. However, in this context there are a few review articles that have been published which belong to almost ten years ago. Hence, we conducted a systematic review to determine the contribution of utilizing fuzzy logic methods in disease diagnosis in different medical practices.
METHODS: Eight scientific databases are selected as an appropriate database and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed as the basis method for conducting this systematic and meta-analysis review. Regarding the main objective of this research, some inclusion and exclusion criteria were considered to limit our investigation. To achieve a structured meta-analysis, all eligible articles were classified based on authors, publication year, journals or conferences, applied fuzzy methods, main objectives of the research, problems and research gaps, tools utilized to model the fuzzy system, medical disciplines, sample sizes, the inputs and outputs of the system, findings, results and finally the impact of applied fuzzy methods to improve diagnosis. Then, we analyzed the results obtained from these classifications to indicate the effect of fuzzy methods in decreasing the complexity of diagnosis.
RESULTS: Consequently, the result of this study approved the effectiveness of applying different fuzzy methods in diseases diagnosis process, presenting new insights for researchers about what kind of diseases which have been more focused. This will help to determine the diagnostic aspects of medical disciplines that are being neglected.
CONCLUSIONS: Overall, this systematic review provides an appropriate platform for further research by identifying the research needs in the domain of disease diagnosis.
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.
A soft computing approach for diabetes disease classification

Nilashi M, Bin Ibrahim O, Mardani A, Ahani A, Jusoh A

Health Informatics J, 2018 12;24(4):379-393.
PMID: 30376769 DOI: 10.1177/1460458216675500

As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use expectation maximization, principal component analysis and support vector machine for clustering, noise removal and classification tasks, respectively. We also develop the proposed method for incremental situation by applying the incremental principal component analysis and incremental support vector machine for incremental learning of data. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction and reduces computation time in relation to the non-incremental approaches. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.
Fulltext Can complementary and alternative medicines be beneficial in the treatment of COVID-19 through improving immune system function?

Nilashi M, Samad S, Yusuf SYM, Akbari E

J Infect Public Health, 2020 Jun;13(6):893-896.
PMID: 32451258 DOI: 10.1016/j.jiph.2020.05.009
Fulltext Parkinson's Disease Diagnosis Using Laplacian Score, Gaussian Process Regression and Self-Organizing Maps

Nilashi M, Abumalloh RA, Alyami S, Alghamdi A, Alrizq M

Brain Sci, 2023 Mar 24;13(4).
PMID: 37190508 DOI: 10.3390/brainsci13040543

Parkinson's disease (PD) is a complex degenerative brain disease that affects nerve cells in the brain responsible for body movement. Machine learning is widely used to track the progression of PD in its early stages by predicting unified Parkinson's disease rating scale (UPDRS) scores. In this paper, we aim to develop a new method for PD diagnosis with the aid of supervised and unsupervised learning techniques. Our method is developed using the Laplacian score, Gaussian process regression (GPR) and self-organizing maps (SOM). SOM is used to segment the data to handle large PD datasets. The models are then constructed using GPR for the prediction of the UPDRS scores. To select the important features in the PD dataset, we use the Laplacian score in the method. We evaluate the developed approach on a PD dataset including a set of speech signals. The method was evaluated through root-mean-square error (RMSE) and adjusted R-squared (adjusted R²). Our findings reveal that the proposed method is efficient in the prediction of UPDRS scores through a set of speech signals (dysphonia measures). The method evaluation showed that SOM combined with the Laplacian score and Gaussian process regression with the exponential kernel provides the best results for R-squared (Motor-UPDRS = 0.9489; Total-UPDRS = 0.9516) and RMSE (Motor-UPDRS = 0.5144; Total-UPDRS = 0.5105) in predicting UPDRS compared with the other kernels in Gaussian process regression.
Fulltext Coronavirus pandemic (COVID-19) and its natural environmental impacts

Rupani PF, Nilashi M, Abumalloh RA, Asadi S, Samad S, Wang S

Int J Environ Sci Technol (Tehran), 2020;17(11):4655-4666.
PMID: 32904898 DOI: 10.1007/s13762-020-02910-x

Coronavirus Disease 2019 (COVID-19) is the official name of a respiratory infectious disease caused by a new coronavirus that started first in Wuhan, China, and outspread worldwide with an unexpectedly fast speed. Flights have been canceled worldwide and transportation has been closed nationwide and across international borders. As a consequence, the economic activity has been stopped and stock markets have been dropped. The COVID-19 lockdown has several social and economic effects. Additionally, COVID-19 has caused several impacts on global migration. On the other hand, such lockdown, along with minimal human mobility, has impacted the natural environment somewhat positively. Overall carbon emissions have dropped, and the COVID-19 lockdown has led to an improvement in air quality and a reduction in water pollution in many cities around the globe. A summary of the existing reports of the environmental impacts of COVID-19 pandemic are discussed and the important findings are presented focusing on several aspects: air pollution, waste management, air quality improvements, waste fires, wildlife, global migration, and sustainability.
Fulltext Role of OpenEHR as an open source solution for the regional modelling of patient data in obstetrics

Pahl C, Zare M, Nilashi M, de Faria Borges MA, Weingaertner D, Detschew V, et al.

J Biomed Inform, 2015 Jun;55:174-87.
PMID: 25900270 DOI: 10.1016/j.jbi.2015.04.004

This work investigates, whether openEHR with its reference model, archetypes and templates is suitable for the digital representation of demographic as well as clinical data. Moreover, it elaborates openEHR as a tool for modelling Hospital Information Systems on a regional level based on a national logical infrastructure. OpenEHR is a dual model approach developed for the modelling of Hospital Information Systems enabling semantic interoperability. A holistic solution to this represents the use of dual model based Electronic Healthcare Record systems. Modelling data in the field of obstetrics is a challenge, since different regions demand locally specific information for the process of treatment. Smaller health units in developing countries like Brazil or Malaysia, which until recently handled automatable processes like the storage of sensitive patient data in paper form, start organizational reconstruction processes. This archetype proof-of-concept investigation has tried out some elements of the openEHR methodology in cooperation with a health unit in Colombo, Brazil. Two legal forms provided by the Brazilian Ministry of Health have been analyzed and classified into demographic and clinical data. LinkEHR-Ed editor was used to read, edit and create archetypes. Results show that 33 clinical and demographic concepts, which are necessary to cover data demanded by the Unified National Health System, were identified. Out of the concepts 61% were reused and 39% modified to cover domain requirements. The detailed process of reuse, modification and creation of archetypes is shown. We conclude that, although a major part of demographic and clinical patient data were already represented by existing archetypes, a significant part required major modifications. In this study openEHR proved to be a highly suitable tool in the modelling of complex health data. In combination with LinkEHR-Ed software it offers user-friendly and highly applicable tools, although the complexity built by the vast specifications requires expert networks to define generally excepted clinical models. Finally, this project has pointed out main benefits enclosing high coverage of obstetrics data on the Clinical Knowledge Manager, simple modelling, and wide network and support using openEHR. Moreover, barriers described are enclosing the allocation of clinical content to respective archetypes, as well as stagnant adaption of changes on the Clinical Knowledge Manager leading to redundant efforts in data contribution that need to be addressed in future works.
Fulltext Latent based temporal optimization approach for improving the performance of collaborative filtering

Al-Hadi IAA, Sharef NM, Sulaiman MN, Mustapha N, Nilashi M

PeerJ Comput Sci, 2020;6:e331.
PMID: 33816980 DOI: 10.7717/peerj-cs.331

Recommendation systems suggest peculiar products to customers based on their past ratings, preferences, and interests. These systems typically utilize collaborative filtering (CF) to analyze customers' ratings for products within the rating matrix. CF suffers from the sparsity problem because a large number of rating grades are not accurately determined. Various prediction approaches have been used to solve this problem by learning its latent and temporal factors. A few other challenges such as latent feedback learning, customers' drifting interests, overfitting, and the popularity decay of products over time have also been addressed. Existing works have typically deployed either short or long temporal representation for addressing the recommendation system issues. Although each effort improves on the accuracy of its respective benchmark, an integrative solution that could address all the problems without trading off its accuracy is needed. Thus, this paper presents a Latent-based Temporal Optimization (LTO) approach to improve the prediction accuracy of CF by learning the past attitudes of users and their interests over time. Experimental results show that the LTO approach efficiently improves the prediction accuracy of CF compared to the benchmark schemes.
Fulltext The impact of coronavirus pandemic (COVID-19) on education: The role of virtual and remote laboratories in education

Abumalloh RA, Asadi S, Nilashi M, Minaei-Bidgoli B, Nayer FK, Samad S, et al.

Technol Soc, 2021 Nov;67:101728.
PMID: 34538984 DOI: 10.1016/j.techsoc.2021.101728

To avoid the spread of the COVID-19 crisis, many countries worldwide have temporarily shut down their academic organizations. National and international closures affect over 91% of the education community of the world. E-learning is the only effective manner for educational institutions to coordinate the learning process during the global lockdown and quarantine period. Many educational institutions have instructed their students through remote learning technologies to face the effect of local closures and promote the continuity of the education process. This study examines the expected benefits of e-learning during the COVID-19 pandemic by providing a new model to investigate this issue using a survey collected from the students at Imam Abdulrahman Bin Faisal University. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed on 179 useable responses. This study applied Push-Pull-Mooring theory and examined how push, pull, and mooring variables impact learners to switch to virtual and remote educational laboratories. The Protection Motivation theory was employed to explain how the potential health risk and environmental threat can influence the expected benefits from e-learning services. The findings revealed that the push factor (environmental threat) is significantly related to perceived benefits. The pull factors (e-learning motivation, perceived information sharing, and social distancing) significantly impact learners' benefits. The mooring factor, namely perceived security, significantly impacts learners' benefits.
Fulltext Recommendation agents and information sharing through social media for coronavirus outbreak

Nilashi M, Asadi S, Minaei-Bidgoli B, Ali Abumalloh R, Samad S, Ghabban F, et al.

Telemat Inform, 2021 Aug;61:101597.
PMID: 34887615 DOI: 10.1016/j.tele.2021.101597

The novel outbreak of coronavirus disease (COVID-19) was an unexpected event for tourism in the world as well as tourism in the Netherlands. In this situation, the travelers' decision-making for tourism destinations was heavily affected by this global event. Social media usage has played an essential role in travelers' decision-making and increased the awareness of travel-related risks from the COVID-19 outbreak. Online consumer media for the outbreak of COVID-19 has been a crucial source of information for travelers. In the current situation, tourists are using electronic word of mouth (eWOM) more and more for travel planning. Opinions provided by peer travelers for the outbreak of COVID-19 tend to reduce the possibility of poor decisions. Nevertheless, the increasing number of reviews per experience makes reading all feedback hard to make an informed decision. Accordingly, recommendation agents developed by machine learning techniques can be effective in the analysis of such social big data for the identification of useful patterns from the data, knowledge discovery, and real-time service recommendations. The current research aims to adopt a framework for the recommendation agents through topic modeling to uncover the most important dimensions of COVID-19 reviews in the Netherland forums in TripAdvisor. This study demonstrates how social networking websites and online reviews can be effective in unexpected events for travelers' decision making. We conclude with the implications of our study for future research and practice.
Fulltext A Hybrid MCDM Approach Based on Fuzzy-Logic and DEMATEL to Evaluate Adult Obesity

Safaei M, A Sundararajan E, Asadi S, Nilashi M, Ab Aziz MJ, Saravanan MS, et al.

Int J Environ Res Public Health, 2022 Nov 22;19(23).
PMID: 36497509 DOI: 10.3390/ijerph192315432

Obesity and its complications is one of the main issues in today's world and is increasing rapidly. A wide range of non-contagious diseases, for instance, diabetes type 2, cardiovascular, high blood pressure and stroke, numerous types of cancer, and mental health issues are formed following obesity. According to the WHO, Malaysia is the sixth Asian country with an adult population suffering from obesity. Therefore, identifying risk factors associated with obesity among Malaysian adults is necessary. For this purpose, this study strives to investigate and assess the risk factors related to obesity and overweight in this country. A quantitative approach was employed by surveying 26 healthcare professionals by questionnaire. Collected data were analyzed with the DEMATEL and Fuzzy Rule-Based methods. We found that lack of physical activity, insufficient sleep, unhealthy diet, genetics, and perceived stress were the most significant risk factors for obesity.
Early diagnosis of Parkinson's disease: A combined method using deep learning and neuro-fuzzy techniques

Nilashi M, Abumalloh RA, Yusuf SYM, Thi HH, Alsulami M, Abosaq H, et al.

Comput Biol Chem, 2023 Feb;102:107788.
PMID: 36410240 DOI: 10.1016/j.compbiolchem.2022.107788

Predicting Unified Parkinson's Disease Rating Scale (UPDRS) in Total- UPDRS and Motor-UPDRS clinical scales is an important part of controlling PD. Computational intelligence approaches have been used effectively in the early diagnosis of PD by predicting UPDRS. In this research, we target to present a combined approach for PD diagnosis using an ensemble learning approach with the ability of online learning from clinical large datasets. The method is developed using Deep Belief Network (DBN) and Neuro-Fuzzy approaches. A clustering approach, Expectation-Maximization (EM), is used to handle large datasets. The Principle Component Analysis (PCA) technique is employed for noise removal from the data. The UPDRS prediction models are constructed for PD diagnosis. To handle the missing data, K-NN is used in the proposed method. We use incremental machine learning approaches to improve the efficiency of the proposed method. We assess our approach on a real-world PD dataset and the findings are assessed compared to other PD diagnosis approaches developed by machine learning techniques. The findings revealed that the approach can improve the UPDRS prediction accuracy and the time complexity of previous methods in handling large datasets.
Fulltext Predicting Parkinson's Disease Progression: Evaluation of Ensemble Methods in Machine Learning

Nilashi M, Abumalloh RA, Minaei-Bidgoli B, Samad S, Yousoof Ismail M, Alhargan A, et al.

J Healthc Eng, 2022;2022:2793361.
PMID: 35154618 DOI: 10.1155/2022/2793361

Parkinson's disease (PD) is a complex neurodegenerative disease. Accurate diagnosis of this disease in the early stages is crucial for its initial treatment. This paper aims to present a comparative study on the methods developed by machine learning techniques in PD diagnosis. We rely on clustering and prediction learning approaches to perform the comparative study. Specifically, we use different clustering techniques for PD data clustering and support vector regression ensembles to predict Motor-UPDRS and Total-UPDRS. The results are then compared with the other prediction learning approaches, multiple linear regression, neurofuzzy, and support vector regression techniques. The comparative study is performed on a real-world PD dataset. The prediction results of data analysis on a PD real-world dataset revealed that expectation-maximization with the aid of SVR ensembles can provide better prediction accuracy in relation to decision trees, deep belief network, neurofuzzy, and support vector regression combined with other clustering techniques in the prediction of Motor-UPDRS and Total-UPDRS.
The nexus between quality of customer relationship management systems and customers' satisfaction: Evidence from online customers' reviews

Nilashi M, Abumalloh RA, Ahmadi H, Samad S, Alrizq M, Abosaq H, et al.

Heliyon, 2023 Nov;9(11):e21828.
PMID: 38034804 DOI: 10.1016/j.heliyon.2023.e21828

Customer Relationship Management (CRM) is a method of management that aims to establish, develop, and improve relationships with targeted customers in order to maximize corporate profitability and customer value. There have been many CRM systems in the market. These systems are developed based on the combination of business requirements, customer needs, and industry best practices. The impact of CRM systems on the customers' satisfaction and competitive advantages as well as tangible and intangible benefits are widely investigated in the previous studies. However, there is a lack of studies to assess the quality dimensions of these systems to meet an organization's CRM strategy. This study aims to investigate customers' satisfaction with CRM systems through online reviews. We collected 5172 online customers' reviews from 8 CRM systems in the Google play store platform. The satisfaction factors were extracted using Latent Dirichlet Allocation (LDA) and grouped into three dimensions; information quality, system quality, and service quality. Data segmentation is performed using Learning Vector Quantization (LVQ). In addition, feature selection is performed by the entropy-weight approach. We then used the Adaptive Neuro Fuzzy Inference System (ANFIS), the hybrid of fuzzy logic and neural networks, to assess the relationship between these dimensions and customer satisfaction. The results are discussed and research implications are provided.
Fulltext Evaluating COVID-19 infection prevention measures in Malaysia: A fuzzy DEMATEL approach

Yadegaridehkordi E, Nilashi M, Nizam Bin Md Nasir MH, Safie Bin Mohd Satar N, Momtazi S

Digit Health, 2023;9:20552076231211670.
PMID: 38074341 DOI: 10.1177/20552076231211670

OBJECTIVE: Since unexpected COVID-19 has been causing massive losses worldwide, preventive measures have been emergency provided to curb the expansion of the epidemic and cut off transmission routes. However, there is a lack of studies that comprehensively address COVID-19 infection prevention measures. This aims to provide a comprehensive evaluation framework to identify the factors impacting COVID-19 infection prevention. Meanwhile, categorizing factors into individual, social, environmental, and technological dimensions and uncovering their interrelationships and level of importance are indeed novelties of this study.
METHODS: An integration of fuzzy logic and decision-making trial and evaluation laboratory (DEMATEL) is utilized, and data was collected from a panel of professional experts in Malaysia. Using a cause-effect relationship diagram, the fuzzy DEMATEL method evaluates the causal relationships between factors.
RESULTS: Findings showed that environmental factors play the most significant roles in preventing COVID-19 infection, followed by technology, individual, and social factors. Getting vaccinated is the most crucial factor in the environmental dimension in cutting the spread of COVID-19. Telehealth, the use of personal protective equipments (PPEs), and the adoption of social distancing are the most important measures in technology, individual and social dimensions, respectively.
CONCLUSIONS: This study offered valuable insights for policymakers and healthcare professionals in designing and implementing effective strategies to prevent pandemic disease transmission. Findings can be practically applied to optimize and prioritize infection prevention measures, assign resources more effectively, and guide evidence-based decision-making in the face of evolving pandemic situations. This process involves the active commitment of all parties, including governments, medical health executives, and citizens.
Fulltext A combined method of optimized learning vector quantization and neuro-fuzzy techniques for predicting unified Parkinson's disease rating scale using vocal features

Zogaan WA, Nilashi M, Ahmadi H, Abumalloh RA, Alrizq M, Abosaq H, et al.

MethodsX, 2024 Jun;12:102553.
PMID: 38292319 DOI: 10.1016/j.mex.2024.102553

Parkinson's Disease (PD) is a common disorder of the central nervous system. The Unified Parkinson's Disease Rating Scale or UPDRS is commonly used to track PD symptom progression because it displays the presence and severity of symptoms. To model the relationship between speech signal properties and UPDRS scores, this study develops a new method using Neuro-Fuzzy (ANFIS) and Optimized Learning Rate Learning Vector Quantization (OLVQ1). ANFIS is developed for different Membership Functions (MFs). The method is evaluated using Parkinson's telemonitoring dataset which includes a total of 5875 voice recordings from 42 individuals in the early stages of PD which comprises 28 men and 14 women. The dataset is comprised of 16 vocal features and Motor-UPDRS, and Total-UPDRS. The method is compared with other learning techniques. The results show that OLVQ1 combined with the ANFIS has provided the best results in predicting Motor-UPDRS and Total-UPDRS. The lowest Root Mean Square Error (RMSE) values (UPDRS (Total)=0.5732; UPDRS (Motor)=0.5645) and highest R-squared values (UPDRS (Total)=0.9876; UPDRS (Motor)=0.9911) are obtained by this method. The results are discussed and directions for future studies are presented.i.ANFIS and OLVQ1 are combined to predict UPDRS.ii.OLVQ1 is used for PD data segmentation.iii.ANFIS is developed for different MFs to predict Motor-UPDRS and Total-UPDRS.
Parkinson's disease diagnosis using deep learning: A bibliometric analysis and literature review

Abumalloh RA, Nilashi M, Samad S, Ahmadi H, Alghamdi A, Alrizq M, et al.

Ageing Res Rev, 2024 Apr;96:102285.
PMID: 38554785 DOI: 10.1016/j.arr.2024.102285

Parkinson's Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis.

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