Nadire ESER, Adem DOĞANER, Selma YAMAN, Tuğba ÖZCAN METİN

Different machine learning methods based prediction of mild cognitive impairment

Aim: In this study benefits from different machine learning methods to analyze factors which affect young person’s scores ofcognitive assessment.Material and Methods: This study was performed among 144 persons aged between 18 and 24 who study at Kahramanmaras SutcuImam University. Boosted Tree Regression (BTR), Random Forest Regression (RFR) and Support Vector Machine (SVM), which areamong machine learning methods, were used in order to determine the factors affecting the score of cognitive assessment. K-10fold cross validation method was also used. Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error(MAE) and Correlation coefficients (R) metrics were used in order to measure prediction performances of machine learning methods.Results: MSE values were calculated as 9.66 for BTR, 9.78 for RFR, and 6.43 for SVM. MAE values were calculated as 2.06 for BTR,2.05 for RFR, and 1.97 for SVM. RMSE values were calculated as 3.10 for BTR, 3.12 for RFR, and 2.53 for SVM. Finally, correlationcoefficients were calculated as 0.289 for BTR, 0.371 for RFR and 0.546 for SVM. In addition, it was also found out that the mostimportant variables which affected the scores of cognitive assessment were anti-depressant use, depression and obsession.Conclusion: It was demonstrated in this study that SVM displayed the lowest error rates and highest prediction performance in termsof determining the score of cognitive assessment. Therefore, SVM can be stated that it is the most suitable method for the predictionof cognitive impairment.

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1. Sachdev PS, Lipnicki DM, Kochan NA, et al. The Prevalence of Mild Cognitive Impairment in Diverse Geographical and Ethnocultural Regions: The COSMIC Collaboration. PLoS ONE 2015;10:e0142388.
2. Plassman BL, Langa KM, Fisher GG, et al. Prevalence of cognitive impairment without dementia in the United States. Ann Intern Med 2008;148:427-34.
3. Mavrodaris A, Powell J, Thorogood M. Prevalences of dementia and cognitive impairment among older people in sub-Saharan Africa: a systematic review. Bulletin of the World Health Organization 2013;91:773- 83.
4. World Health Organization. The world health report: primary health care now more than ever. Geneva, 2008.
5. DeCarli C. Mild cognitive impairment: prevalence, prognosis, aetiology, and treatment. The Lancet Neurology 2003;2:15-21.
6. Castaneda AE, Tuulio-Henriksson A, Marttunen M, et al. Review on cognitive impairments in depressive and anxiety disorders with a focus on young adults. J Affective Disorders 2008;106:1-27.
7. Porter RJ, Gallagher P, Thompson, JM. et al. Neurocognitive impairment in drug-free patients with major depressive disorder. British J Psychiatry 2003;182: 214-20.
8. Burns JM, Andrews G, Szabo M. Depression in young people: what causes it and can we prevent it? Medical J Australia 2002;177:93-6.
9. Thompson PJ, Trimble MR. Non-MAOI antidepressant drugs and cognitive functions: a review. Psychological Med 1982;12:539-48.
10. Shin NY, Lee TY, Kim E, et al. Cognitive functioning in obsessive-compulsive disorder: a meta-analysis. Psychological Med 2014;44:1121-30.
11. Nilsson NJ. Introduction to machine learning: An early draft of a proposed textbook. Stanford, Stanford University 1996.
12. Van der Meer D, Hoekstra PJ, Van Donkelaar M, et al. Predicting attention-deficit/hyperactivity disorder severity from psychosocial stress and stressresponse genes: a random forest regression approach. Translational Psychiatry 2017;7:1145.
13. Lee BK, Lessler J, Stuart EA. Improving propensity score weighting using machine learning. Statistics in Med 2010;29:337-46.
14. Månsson KN, Frick A, Boraxbekk CJ, et al. Predicting long-term outcome of Internet-delivered cognitive behavior therapy for social anxiety disorder using fMRI and support vector machine learning. Translational Psychiatry 2015;5:530.
15. O’Dwyer L, Lamberton F, Bokde AL, et al. Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment. PloS one 2012;7:32441.
16. Arslan AK, Colak C, Sarihan ME. Different medical data mining approaches based prediction of ischemic stroke. Comput Methods Programs Biomed 2016;130:87-92.
17. Dreiseitl S, Ohno-Machado L, Kittler H, et al. Comparison of machine learning methods for the diagnosis of pigmented skin lesions. J Biomed Inform 2001;34:28-36.
18. Nahar J, Imam T, Tickle KS, et al. Computational intelligence for heart disease diagnosis: A medical knowledge driven approach. Expert Syst Appl 2013;40:96-104.
19. Asri H, Mousannif H, Al Moatassime H, et al. Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput Sci2016; 83:1064-1069.
20. Munteanu CR, Fernandez-Lozano C, Abad VM, et al. Classification of mild cognitive impairment and Alzheimer’s Disease with machine-learning techniques using 1H Magnetic Resonance Spectroscopy data. Expert Syst Appl 2015;42:6205-14.
21. Chen R, Herskovits EH. Machine-learning techniques for building a diagnostic model for very mild dementia. Neuroimage 2010;52:234-44.
22. So A, Hooshyar D, Park K, et al. Early diagnosis of dementia from clinical data by machine learning techniques. Applied Sciences 2017;7:651.
23. Weakley A, Williams JA, Schmitter-Edgecombe M, et al. Neuropsychological test selection for cognitive impairment classification: a machine learning approach. J Clin Exp Neuropsychol 2015;37:899-916.
24. Selekler K, Cangöz B, Uluç S. Montreal Bilişsel Değerlendirme Ölçeği (MOBİD)’nin hafif bilişsel bozukluk ve Alzheimer hastalarını ayırt edebilme gücünün incelenmesi. Turkish Journal of Geriatrics 2010;13:166-71.
25. Nasreddine ZS, Phillips NA, Bédirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005;53:695-9.
26. Elith J, Leathwick JR, Hastie T. A working guide to boosted regression trees. J Anim Ecol 2008;77:802- 13.
27. Polikar R. Ensemble Learning. In: Zhang C, Ma Y, eds. Ensemble machine learning: methods and applications. New York, USA:Springer Science & Business Media 2012:1-34.
28. Breiman L. Random forests. Machine learning 2001;45:5-32.
29. Drucker H, Burges CJ, Kaufman L, et al. Support vector regression machines.Adv Neural Inf Process Syst 1997;155-61.
30. Shirmohammadi-Khorram N, Tapak L, Hamidi O, et al. A comparison of three data mining time series models in prediction of monthly brucellosis surveillance data. Zoonoses Public Health 2019.
31. Khalid SG, Zhang J, Chen F, et al. Blood pressure estimation using photoplethysmography only: comparison between different machine learning approaches. J Healthc Eng 2018;13.
32. Valizadeh M, Sohrabi MR. The application of artificial neural networks and support vector regression for simultaneous spectrophotometric determination of commercial eye drop contents. Spectrochim Acta A Mol Biomol Spectrosc 2018;193:297-304.
33. Guo P, Liu T, Zhang Q, et al. Developing a dengue forecast model using machine learning: A case study in China. PLoS neglected tropical diseases 2017;11: 5973.
34. Liu R, Li X, Zhang W, et al. Comparison of nine statistical model based warfarin pharmacogenetic dosing algorithms using the racially diverse international warfarin pharmacogenetic consortium cohort database. PloS one 2015;10:0135784.
35. Swardfager W, MacIntosh BJ. Depression, Type 2 Diabetes, and Poststroke Cognitive Impairment. Neurorehabil Neural Repair 2017;31:48-55.
36. Albert MS, DeKosky ST, Dickson D, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia 2011;7:270-9.
37. Farina N, Llewellyn D, Isaac MGEKN, et al. Vitamin E for Alzheimer’s dementia and mild cognitive impairment. Cochrane Database Syst Rev 2017;1.
38. Li JQ, Tan L, Wang HF, et al. Risk factors for predicting progression from mild cognitive impairment to Alzheimer’s disease: a systematic review and metaanalysis of cohort studies. J Neurol Neurosurg Psychiatry 2016; 87:476-84.