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Annals of Data Science

08-06-2024

Improving Dementia Prediction Using Ensemble Majority Voting Classifier

Authors: K. P. Muhammed Niyas, P. Thiyagarajan

Published in: Annals of Data Science

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Abstract

Early detection of dementia patients in advance is a great concern for the physicians. That is why physicians make use of multi modal data to accomplish this. The baseline visit data of the patients are mainly utilized for this task. Modern Machine Learning techniques provide empirical evidence based approach to physicians for predicting the diagnosis status of the patients. This paper proposes an ensemble majority voting classifier approach for improving the detection of dementia using baseline visit data. The ensemble model consists of Logistic Regression, Random Forest, and Naive Bayes Classifiers. The proposed ensemble classifier reported with a BCA, F1-score of 92%, 0.92 for classifying demented and non-demented patients. Our results suggest that the prediction using the ensemble majority voting classifier improves the Balanced Classification Accuracy, F1-score for predicting dementia on the multi modal data of Open Access Series Imaging Dataset. The results using ensemble models are promising and highlight the importance of using ensemble models for dementia detection using multimodal data.

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Literature
1.
Shi Y, Tian Y, Kou G, Peng Y, Li J (2011) Optimization based data mining: theory and applications. Springer Science & Business Media
2.
Tien JM (2017) Internet of things, real-time decision making, and artificial intelligence. Ann Data Sci 4:149–178CrossRef
3.
Olson DL, Shi Y, Shi Y (2007) Introduction to business data mining. McGraw-Hill/Irwin New York vol. 10
4.
Amar LA, Taha AA, Mohamed MY (2020) Prediction of the final size for covid-19 epidemic using machine learning: a case study of egypt. Infect Disease Model 5:622–634CrossRef
5.
Fatima M, Pasha M (2017) Survey of machine learning algorithms for disease diagnostic. J Intell Learn Syst Appl 9(01):1–16
6.
Nelson A, Herron D, Rees G, Nachev P (2019) Predicting scheduled hospital attendance with artificial intelligence. NPJ Digit Med 2(1):26CrossRef
7.
Spyropoulos CD (2000) Ai planning and scheduling in the medical hospital environment. Artif Intell Med 20:101–111CrossRef
8.
Wang P, Zheng X, Li J, Zhu B (2020) Prediction of epidemic trends in covid-19 with logistic model and machine learning technics. Chaos, Solitons Fractals 139:110058CrossRef
9.
Schmidt CW (2012) Trending now: using social media to predict and track disease outbreaks
10.
Long JB, Ehrenfeld JM (2020) The role of augmented intelligence (ai) in detecting and preventing the spread of novel coronavirus. J Med Syst 44:1–2CrossRef
11.
Chen M, Hao Y, Hwang K, Wang L, Wang L (2017) Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5:8869–8879CrossRef
12.
Manogaran G, Vijayakumar V, Varatharajan R, Malarvizhi Kumar P, Sundarasekar R, Hsu C-H (2018) Machine learning based big data processing framework for cancer diagnosis using hidden Markov model and gm clustering. Wirel Personal Commun 102:2099–2116CrossRef
13.
Jayasuriya D, Chan J, Sundaram D (2020) Big data analytics using smalldatasets: machine learning for early breast cancer detection
14.
Lv Z, Qiao L (2020) Analysis of healthcare big data. Future Gen Com put Syst 109:103–110CrossRef
15.
Shafqat S, Kishwer S, Rasool RU, Qadir J, Amjad T, Ahmad HF (2020) Big data analytics enhanced healthcare systems: a review. J Supercomput 76:1754–1799CrossRef
16.
Galetsi P, Katsaliaki K, Kumar S (2020) Big data analytics in health sector: theoretical framework, techniques and prospects. Int J Inf Manage 50:206–216CrossRef
17.
Lynch C (2020) World alzheimer report 2019: attitudes to dementia, a global survey: public health: engaging people in adrd research. Alzheimer’s Dementia 16:e038255CrossRef
18.
G. Mellor, “Dealing with alzheimer’s disease,” 2020.
19.
Frank L, Shubeck E, Schicker M, Webb T, Maslow K, Gitlin L, Hummel CH, Kaplan EK, LeBlanc B, Marquez M et al (2020) Contributions of persons living with dementia to scientific research meetings. results from the national research summit on care, services, and supports for persons with dementia and their caregivers. Am J Geriatr Psychiatr 28(4):421–430CrossRef
20.
Ding J, Davis-Plourde KL, Sedaghat S, Tully PJ, Wang W, Phillips C, Pase MP, Himali JJ, Windham BG, Griswold M et al (2020) Antihyper- tensive medications and risk for incident dementia and alzheimer’s disease: a meta-analysis of individual participant data from prospective cohort studies. Lancet Neurol 19(1):61–70CrossRef
21.
Rausch C, Hoffmann F (2020) Prescribing medications of questionable benefit prior to death: a retrospective study on older nursing home residents with and without dementia in Germany. Eur J Clin Pharmacol 76:877–885CrossRef
22.
Stoehr GP, Jacobsen E, Jia Y, Snitz BE, Ganguli M (2020) Trends in the use of medications and supplements to treat or prevent dementia: a population-based study. Alzheimer Dis Assoc Disord 34(2):148CrossRef
23.
Castellazzi G, Cuzzoni MG, Cotta Ramusino M, Martinelli D, Denaro F, Ricciardi A, Vitali P, Anzalone N, Bernini S, Palesi F et al (2020) A machine learning approach for the differential diagnosis of alzheimer and vascular de- mentia fed by mri selected features. Front Neuroinform 14:25CrossRef
24.
Mather M (2020) How do cognitively stimulating activities affect cognition and the brain throughout life? Psychol Sci Public Interest 21(1):1–5CrossRef
25.
Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, Brayne C, Burns A, Cohen-Mansfield J, Cooper C et al (2020) Dementia pre- vention, intervention, and care: 2020 report of the lancet commission. The Lancet 396(10248):413–446CrossRef
26.
Rostamzadeh A, Jessen F (2020) Early detection of alzheimers disease and dementia prediction in patients with mild cognitive impairment: summary of current recommendations. Nervenarzt 91:832–842CrossRef
27.
Roheger M, Meyer J, Kessler J, Kalbe E (2020) Predicting short-and long- term cognitive training success in healthy older adults: Who benefits? Aging Neuropsychol Cogn 27(3):351–369CrossRef
28.
Bessey LJ, Walaszek A (2019) Management of behavioral and psychological symptoms of dementia. Curr Psychiatr Rep 21:1–11CrossRef
29.
Vik-Mo AO, Giil LM, Ballard C, Aarsland D (2018) Course of neuropsy- chiatric symptoms in dementia: 5-year longitudinal study. Int J Geriatric Psychiatr 33(10):1361–1369CrossRef
30.
Licher S, Leening MJ, Yilmaz P, Wolters FJ, Heeringa J, Bindels PJ, Initiative ADN, Vernooij MW, Stephan BC, Steyerberg EW et al (2019) Development and validation of a dementia risk prediction model in the general population: an analysis of three longitudinal studies. Am J Psychiatr 176(7):543–551CrossRef
31.
Jennings LA, Laffan AM, Schlissel AC, Colligan E, Tan Z, Wenger NS, Reuben DB (2019) Health care utilization and cost outcomes of a comprehensive dementia care program for medicare beneficiaries. JAMA Intern Med 179(2):161–166CrossRef
32.
Alkabawi EM, Hilal AR, Basir OA (2017) Computer-aided classification of multi-types of dementia via convolutional neural networks. In 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE, 2017, pp. 45–50
33.
Varatharajan R, Manogaran G, Priyan MK, Sundarasekar R (2018) Wear- able sensor devices for early detection of alzheimer disease using dynamic time warping algorithm. Clust Comput 21:681–690CrossRef
34.
Kp MN, Thiyagarajan P (2022) Feature selection using efficient fusion of fisher score and greedy searching for alzheimer’s classification. J King Saud Univ Comput Inf Sci 34(8):4993–5006
35.
Shakarami A, Tarrah H, Mahdavi-Hormat A (2020) A cad system for diagnosing alzheimer’s disease using 2d slices and an improved alexnet-svm method. Optik 212:164237CrossRef
36.
Ruiz E, Ramirez J, Gorriz JM, Casillas J, Initiative ADN et al (2018) Alzheimer’s disease computer-aided diagnosis: histogram-based analysis of regional mri volumes for feature selection and classification. J Alzheimer’s Disease 65(3):819–842CrossRef
37.
Khan NM, Abraham N, Hon M (2019) Transfer learning with intelligent training data selection for prediction of alzheimer’s disease. IEEE Access 7:72726–72735CrossRef
38.
Amin Al Olama A, Wason JM, Tuladhar AM, van Leijsen EM, Koini M, Hofer E, Morris RG, Schmidt R, de Leeuw F-E, Markus HS (2020) Simple MRI score aids prediction of dementia in cerebral small vessel disease. Neurology 94(12):1294–1302CrossRef
39.
Mathotaarachchi S, Pascoal TA, Shin M, Benedet AL (2017) Min su kang, thomas beaudry, vladimir s. fonov, serge gauthier, pedro rosa-neto, and alzheimer’s disease neuroimaging initiative. Identifying incipient dementia individuals using machine learning and amyloid imaging. Neurobiol Aging 59:80–90CrossRef
40.
Guo J, Qiu W, Li X, Zhao X, Guo N, Li Q (2019) Predicting alzheimer’sdisease by hierarchical graph convolution from positron emission tomography imaging. In: 2019 IEEE international conference on big data (big data). IEEE, 2019, pp. 5359–5363
41.
Adel M, Garali I, Pan X, Fossati, C, Gaidon T, Wojak J, Bourennane S,Guedj E (2019) Alzheimer’s disease computer-aided diagnosis on positron emission tomography brain images using image processing techniques. Comput Methods Prog Biomed Signal Image Process p. 13
42.
El-Sappagh S, Abuhmed T, Islam SR, Kwak KS (2020) Multimodal multitask deep learning model for alzheimer’s disease progression detection based on time series data. Neurocomputing 412:197–215CrossRef
43.
Zhou T, Thung K-H, Zhu X, Shen D (2019) Effective feature learning and fusion of multimodality data using stage-wise deep neural network for demen-tia diagnosis. Hum Brain Mapp 40(3):1001–1016CrossRef
44.
Forouzannezhad P, Abbaspour A, Li C, Fang C, Williams U, Cabrerizo M, Barreto A, Andrian J, Rishe N, Curiel RE et al (2020) A gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging. J Neurosci Methods 333:108544CrossRef
45.
Khagi B, Lee B, Pyun J-Y, Kwon G-R (2019) Cnn models performanceanalysis on mri images of oasis dataset for distinction between healthy and alzheimer’s patient. In: 2019 International Conference on Electronics, Infor- mation, and Communication (ICEIC). IEEE pp. 1–4
46.
Islam J, Zhang Y (2017) An ensemble of deep convolutional neural networks for alzheimer’s disease detection and classification. arXiv preprintarXiv:​1712.​01675
47.
Khan A, Zubair S (2022) An improved multi-modal based machine learning approach for the prognosis of alzheimer’s disease. J King Saud Univ Comput Inf Sci 34(6):2688–2706
48.
Battineni G, Chintalapudi N, Amenta F (2019) Machine learning in medicine: performance calculation of dementia prediction by support vector machines (svm). Inf Med Unlocked 16:100200CrossRef
49.
Bansal D, Chhikara R, Khanna K, Gupta P (2018) Comparative analysis of various machine learning algorithms for detecting dementia. Procedia Comput Sci 132:1497–1502CrossRef
50.
Miah Y, Prima CNE, Seema SJ, Mahmud M, Shamim Kaiser M (2021) Performance comparison of machine learning techniques in identifying dementia from open access clinical datasets. In: Advances on Smart and Soft Computing: Proceedings of ICAC. Springer, 2021, pp. 79–89
51.
Marcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL (2007) Open access series of imaging studies (oasis): cross-sectional mri data in young, middle aged, nondemented, and demented older adults. J Cogn Neurosci 19(9):1498–1507CrossRef
52.
Kotsiantis SB, Kanellopoulos D, Pintelas PE (2006) Data preprocessing for supervised leaning. Int J Comput Sci 1(2):111–117
53.
Jadhav A, Pramod D, Ramanathan K (2019) Comparison of performance of data imputation methods for numeric dataset. Appl Artif Intell 33(10):913–933CrossRef
54.
Beaulieu-Jones BK, Moore JH, PROAACT CONSORTIUM (2017) Missing data imputation in the electronic health record using deeply learned autoencoders. In: Pacific symposium on biocomputing.World Scientific, 2017, pp. 207–218
55.
Kira K, Rendell LA (1992) A practical approach to feature selection. In: Machine learning proceedings 1992. Elsevier, 1992, pp. 249–256
56.
Hall MA (1996) Correlation-based feature selection for machine learning. Ph.D. dissertation, The University of Waikato
57.
Hall MA (2000) Correlation-based feature selection of discrete and numeric class machine learning
58.
Bruce P, Bruce A, Gedeck P (2020) Practical statistics for data scientists: 50+essential concepts using R and Python. O’Reilly Media
59.
Ng AY et al. (1997) Preventing overfitting of cross-validation data. In: ICML, vol. 97. Citeseer, pp. 245–253
60.
Ghojogh B, Crowley M (2019)The theory behind overfitting, cross validation, regularization, bagging, and boosting: tutorial. arXiv preprint arXiv:​1905.​12787
61.
Li X, Zhu D, Levy P (2020) Predicting clinical outcomes with patient strati- fication via deep mixture neural networks. In: AMIA Summits on Translational Science Proceedings, vol. 2020, p. 367
62.
Santos MS, Soares JP, Abreu PH, Araujo H, Santos J (2018) Cross-validation for imbalanced datasets: avoiding overoptimistic and overfitting approaches [research frontier]. IEEE Comput Intell Magaz 13(4):59–76CrossRef
63.
Rakotomamonjy A, Flamary R, Salmon J, Gasso G (2022) Convergent working set algorithm for lasso with non-convex sparse regularizers. In: International Conference on Artificial Intelligence and Statistics. PMLR, pp. 5196–5211
64.
DeMaris A (1995) A tutorial in logistic regression. J MarriageFamily pp. 956–968
65.
Osuna E, Freund R, Girosi F (1997) An improved training algorithm forsupport vector machines. In: Neural networks for signal processing VII. Proceedings of the 1997 IEEE signal processing society workshop. IEEE, 1997, pp. 276–285
66.
Bi J, Zhang T (2004) Support vector classification with input data uncertainty. Adv Neural Inf Process Syst vol. 17
67.
Denisko D, Hoffman MM (2018) Classification and interaction in random forests. Proc Natl Acad Sci 115(8):1690–1692CrossRef
68.
Berrar D (2018) Bayes’ theorem and naive bayes classifier. Encyclopedia Bioinform Computat Biol ABC Bioinform 403:412
69.
Rish I et al. (2001) An empirical study of the naive bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence, 3(22):41–46
70.
71.
Prasath V, Alfeilat HAA, Hassanat A, Lasassmeh O, Tarawneh AS, Alhasanat MB, Salman HSE (2017) Distance and similarity measures effect on the performance of k-nearest neighbor classifier–a review. arXiv preprint arXiv:​1708.​04321
72.
Romera-Paredes B, Aung MS, Bianchi-Berthouze N (2013) A one-vs-oneclassifier ensemble with majority voting for activity recognition. In: Esann
73.
Raza K (2019) Improving the prediction accuracy of heart disease with ensem- ble learning and majority voting rule. In: U-Healthcare Monitoring Systems. Elsevier pp. 179–196
74.
Kim H, Kim H, Moon H, Ahn H (2011) A weight-adjusted voting algorithm for ensembles of classifiers. J Korean Statist Soc 40(4):437–449CrossRef
75.
Feng X, Xiao Z, Zhong B, Qiu J, Dong Y (2018) Dynamic ensemble classification for credit scoring using soft probability. Appl Soft Comput 65:139–151CrossRef
76.
Ravi V, Kurniawan H, Thai PNK, Kumar PR (2008) Soft computing system for bank performance prediction. Appl Soft Comput 8(1):305–315CrossRef
77.
Brodersen KH, Ong CS, Stephan KE, Buhmann JM (2010) The balanced accuracy and its posterior distribution. In: 2010 20th international con- ference on pattern recognition. IEEE, 2010, pp. 3121–3124
Metadata
Title
Improving Dementia Prediction Using Ensemble Majority Voting Classifier
Authors
K. P. Muhammed Niyas
P. Thiyagarajan
Publication date
08-06-2024
Publisher
Springer Berlin Heidelberg
Published in
Annals of Data Science
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-024-00550-3

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