Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm

Computer Methods and Programs in Biomedicine

Volumn 152, Issue , 2017, Pages 23-34

  Maniruzzaman, Md ^a   Kumar, Nishith ^b   Menhazul Abedin, Md ^c   Shaykhul Islam, Md ^a   Suri, Harman S ^d   El Baz, Ayman S ^e   Suri, Jasjit S ^d,f  

^a UNIVERSITY OF RAJSHAHI   (Bangladesh)

^b BANGABANDHU SHEIKH MUJIBUR RAHMAN SCIENCE AND TECHNOLOGY UNIVERSITY   (Bangladesh)

^c KHULNA UNIVERSITY   (Bangladesh)

^d AtheroPoint LLC   (United States)

^e University of Louisville   (United States)

^f Nucl Eng Idaho State University   (United States)

Author keywords

Diabetes; GPC; LDA; Machine learning; NB; QDA

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA HANDLING; DISCRIMINANT ANALYSIS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); LEARNING ALGORITHMS; MEDICAL PROBLEMS; NIOBIUM;

CLASSIFICATION TECHNIQUE; GAUSSIAN PROCESS CLASSIFICATIONS; LINEAR DISCRIMINANT ANALYSES (LDA); NEGATIVE PREDICTIVE VALUE; POSITIVE PREDICTIVE VALUES; QUADRATIC DISCRIMINANT ANALYSIS; RECEIVER OPERATING CHARACTERISTIC (ROC) CURVES; WORLD HEALTH ORGANIZATION;

LEARNING SYSTEMS;

ACCURACY; ARTICLE; BAYESIAN LEARNING; BENCHMARKING; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; DATA ANALYSIS; DIABETES MELLITUS; DISCRIMINANT ANALYSIS; DISEASE CLASSIFICATION; GAUSSIAN PROCESS CLASSIFICATION; HUMAN; INDIAN; INTERMETHOD COMPARISON; JACKKNIFE TEST; KERNEL METHOD; MACHINE LEARNING; MAJOR CLINICAL STUDY; MATHEMATICAL COMPUTING; PERFORMANCE MEASUREMENT SYSTEM; PREDICTIVE VALUE; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; ALGORITHM; BAYES THEOREM; CLASSIFICATION; COMPARATIVE STUDY; REPRODUCIBILITY;

ALGORITHMS; BAYES THEOREM; DIABETES MELLITUS; DISCRIMINANT ANALYSIS; HUMANS; MACHINE LEARNING; REPRODUCIBILITY OF RESULTS;

EID: 85029347697     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2017.09.004     Document Type: Article

References (41)

1. Association, AmericanDiabetes, Diagnosis and classification of diabetes mellitus. Diabetes Care 32:Supplement 1 (2009), S62–S67.
2. Franz, M.J., A Core Curriculum for Diabetes Education: Diabetes Management Therapies. Vol. 2, 2003, American Association of Diabetes Educator.
3. Tao, Z., Shi, A., Zhao, J., Epidemiological perspectives of diabetes. Cell Biochem. Biophys. 73:1 (2015), 181–185.
4. Chiang, J.L., Kirkman, M.S., Laffel, L.M.B., Peters, A.L., Type 1 diabetes through the life span: a position statement of the american diabetes association. Diabetes Care 37:7 (2014), 2034–2054.
5. Begum, S.A., Afroz, R., Khanam, Q., Khanom, A., Choudhury, T.S., Diabetes mellitus and gestational diabetes mellitus. J. Paediatric Surgeons Bangladesh 5:1 (2015), 30–35.
6. Canadian Diabetes Association and Diabetes Qu´ebec, Diabetes: Canada at the Tipping Point: Charting a New Path. 2011, Canadian Diabetes Association.
7. Centers Disease Control, Prevention (CDC), Centers for Disease Control, and Prevention (CDC). National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the united states, 2011. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, 201, 2011.
8. Barakat, M.N.H., Bradley, A.P., Intelligible support vector machines for diagnosis of diabetes mellitus. Inf. Technol. Biomed. IEEE Trans. 14:4 (2010), 1114–1120.
9. kumar Dewangan, A., Agrawal, P., Classification of diabetes mellitus using machine learning techniques. Int. J. Eng. Appl. Sci. 2:5 (2015), 145–148.
10. Lucatelli, P., Montisci, R., Sanfilippo, R., Sacconi, B., Suri, J.S., Catalano, C., Saba, L., Is there an association between leukoaraiosis volume and diabetes?. J. Neuroradiol. 57:12 (2015), 1181–1282.
11. Acharya, U.R., Faust, O., Kadri, N.A., Suri, J.S., Yu, W., Automated identification of normal and diabetes heart rate signals using nonlinear measures. Comput. Biol. Med. 43:10 (2013), 1523–1529.
12. Saba, L., Ikeda, N., Deidda, M., Araki, T., Molinari, F., Meiburger, K.M., Acharya, U.R., Nagashima, Y., Mercuro, G., Nakano, M., Association of automated carotid imt measurement and hba1c in Japanese patients with coronary artery disease. Diabetes Res. Clin. Pract. 100:3 (2013), 348–353.
13. Acharya, U.R., Tong, J., Subbhuraam, V.S., Chua, C.K., Ha, T.P., Ghista, D.N., Chattopadhyay, S., Ng, K.-H., Suri, J.S., Computer-based identification of type 2 diabetic subjects with and without neuropathy using dynamic planter pressure and principal component analysis. J. Med. Syst. 36:4 (2012), 2483–2491.
14. Faust, O., Acharya, R., Ng, E.Y.K., Ng, K.-H., Suri, J.S., Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review. J. Med. Syst. 36:1 (2012), 145–157.
15. Williams, C.K.I., Rasmussen, C.E., Gaussian Processes for Machine Learning. the MIT Press, 2(3), 2006, 4.
16. Opper, M., Winther, O., Gaussian processes for classification: mean-field algorithms. Neural Comput. 12:11 (2000), 2655–2684.
17. Jain, A.K., Duin, R.P.W., Mao, J., Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22:1 (2000), 4–37.
18. Fisher, R.A., The use of multiple measurements in taxonomic problems. Ann. Hum. Genet. 7:2 (1936), 179–188.
19. Smith, C.A., Some examples of discrimination. Ann. Hum. Genet. 13:1 (1946), 272–282.
20. Marzio, M.D., Taylor, C.C., Kernel density classification and boosting: an l 2 analysis. Stat. Comput. 15:2 (2005), 113–123.
21. C.L. Blake, C.J. Merz, UCI repository of machine learning databases. University of California, Department of Information and Computer Science, Irvine, CA (1998), See: http://www.ics.uci.edu/mlearn/MLRepository.html (2003).
22. Edgar Acuna, E., Rodriguez, C., The treatment of missing values and its effect in the classifier accuracy. Proceedings of the Meeting of the International Federation of Classification Societies (IFCS), 2004, 639–647.
23. Cokluk, O., Kayri, M., The effects of methods of imputation for missing values on the validity and reliability of scales. Edu. Sci.: Theor. Pract. 11:1 (2011), 303–309.
24. Baneshi, M.R., Talei, A.R., Does the missing data imputation method affect the composition and performance of prognostic models?. Iran. Red. Crescent Med. J., 14(1), 2012, 31.
25. Kaiser, J., Dealing with missing values in data. J. Syst. Integration, 5(1), 2014, 42.
26. Zainuri, N.A., Jemain, A.A., Muda, N., A comparison of various imputation methods for missing values in air quality data. Sains Malays. 44:3 (2015), 449–456.
27. Polat, K., Güneş, S., An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digit Signal Process. 17:4 (2007), 702–710.
28. Deng, D., Kasabov, N., On-line pattern analysis by evolving self-organizing maps. Neurocomputing 51:2 (2003), 87–103.
29. Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., Johannes, R.S., Using the adap learning algorithm to forecast the onset of diabetes mellitus. Proceedings of the Annual Symposium on Computer Application in Medical Care, American Medical Informatics Association, 1988, 261.
30. Quinlan, J.R., C4. 5: Programs for Machine Learning. 2014, Elsevier.
31. Sahan, S.S., Polat, K., Kodaz, H., Güneş, S., The medical applications of attribute weighted artificial immune system (AWAIS): diagnosis of heart and diabetes diseases. Artif. Immune Syst., 2005, 456–468.
32. Christobel, Y.A., Sivaprakasam, P., A new classwise k nearest neighbor (CKNN) method for the classification of diabetes dataset. Int. J. Eng. Adv. Technol. (IJEAT) 2:3 (2013), 396–400.
33. Parashar, A., Burse, K., Rawat, K., A comparative approach for pima indians diabetes diagnosis using LDA-support vector machine and feed forward neural network. Int. J. Adv. Res. Comput. Sci. Software Eng. 4:11 (2014), 378–383.
34. Farahmandian, M., Lotfi, Y., Maleki, I., Data Mining Algorithms Application in Diabetes Diseases Diagnosis: A Case Study. Technical Report, 2015, MAGNT Research Report.
35. Khashei, M., Eftekhari, S., Parvizian, J., Diagnosing diabetes type ii using a soft intelligent binary classification model. Rev. Bioinf. Biometrics 1:1 (2012), 9–23.
36. Bozkurt, M.R., Yurtay, N., Yilmaz, Z., Sertkaya, C., Comparison of different methods for determining diabetes. Turkish J. Electr. Eng. Comput. Sci. 22:4 (2014), 1044–1055.
37. Kumari, V.A., Chitra, R., Classification of diabetes disease using support vector machine. Int. J. Eng. Res. Appl. 3:2 (2013), 1797–1801.
38. DeLeo, J.M., Receiver operating characteristic laboratory (ROCLAB): software for developing decision strategies that account for uncertainty. Uncertainty Modeling and Analysis, 1993. Proceedings, Second International Symposium on, IEEE, 1993, 318–325.
39. A. Iyer, S. Jeyalatha, R. Sumbaly, Diagnosis of diabetes using classification mining techniques, arXiv preprint arXiv:1502.03774 (2015).
40. Williams, C.K.I., Barber, D., Bayesian classification with gaussian processes. Pattern Anal. Mach. Intell. IEEE Trans. 20:12 (1998), 1342–1351.
41. Valletta, J.J., Chipperfield, A.J., Byrne, C.D., Gaussian process modelling of blood glucose response to free-living physical activity data in people with type 1 diabetes. Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, IEEE, 2009, 4913–4916.