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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.

Matched MeSH terms: Diabetes Mellitus/classification*
Fulltext A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network

Salari N, Shohaimi S, Najafi F, Nallappan M, Karishnarajah I

PLoS One, 2014;9(11):e112987.
PMID: 25419659 DOI: 10.1371/journal.pone.0112987

Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher's discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the proposed model in terms of classification accuracy is desirable, promising, and competitive to the existing state-of-the-art classification models.

Matched MeSH terms: Diabetes Mellitus/classification
Validation of the comprehensive ICF core sets for diabetes mellitus:a Malaysian perspective

Abdullah MF, Nor NM, Mohd Ali SZ, Ismail Bukhary NB, Amat A, Latif LA, et al.

Ann Acad Med Singap, 2011 Apr;40(4):168-78.
PMID: 21678002

INTRODUCTION: Diabetes mellitus (DM) is a chronic disease that is prevalent in many countries. The prevalence of DM is on the rise, and its complications pose a heavy burden on the healthcare systems and on the patients' quality of life worldwide.
MATERIALS AND METHODS: This is a multicentre, cross-sectional study involving 5 Health Clinics conducted by Family Medicine Specialists in Malaysia. Convenience sampling of 100 respondents with DM were selected. The International Classifi cation of Functioning, Disability and Health (ICF) based measures were collected using the Comprehensive Core Set for DM. SF-36 and self-administered forms and comorbidity questionnaire (SCQ) were also used.
RESULTS: Ninety-seven percent had Type 2 DM and 3% had Type 1 DM. The mean period of having DM was 6 years. Body functions related to physical health including exercise tolerance (b455), general physical endurance (b4550), aerobic capacity (b4551) and fatiguability (b4552) were the most affected. For body structures, the structure of pancreas (s550) was the most affected. In the ICF component of activities and participation, limitation in sports (d9201) was the highest most affected followed by driving (d475), intimate relationships (d770), handling stress and other psychological demands (d240) and moving around (d455). Only 7% (e355 and e450) in the environmental category were documented as being a relevant factor by more than 90% of the patients.
CONCLUSION: The content validity of the comprehensive ICF Core set DM for Malaysian population were identified and the results show that physical and mental functioning were impaired in contrast to what the respondents perceived as leading healthy lifestyles.

Matched MeSH terms: Diabetes Mellitus/classification*

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