Comprehensive Performance Analysis of Neurodegenerative disease Incidence in the Females of 60-96 year Age Group

  • Afreen Khan
    Aligarh Muslim University afreen.khan2k13[at]
  • Swaleha Zubair
    Aligarh Muslim University
  • Samreen Khan
    Integral Institute of Medical Sciences and Research


Neurodegenerative diseases such as Alzheimer’s disease and dementia are gradually becoming more prevalent chronic diseases, characterized by the decline in cognitive and behavioral symptoms. Machine learning is revolu-tionising almost all domains of our life, including the clinical system. The application of machine learning has the potential to enormously augment the reach of neurodegenerative care thus building it more proficient. Throughout the globe, there is a massive burden of Alzheimer’s and demen-tia cases; which denotes an exclusive set of difficulties. This provides us with an exceptional opportunity in terms of the impending convenience of data. Harnessing this data using machine learning tools and techniques, can put scientists and physicians in the lead research position in this area. The ob-jective of this study was to develop an efficient prognostic ML model with high-performance metrics to better identify female candidate subjects at risk of having Alzheimer’s disease and dementia. The study was based on two diverse datasets. The results have been discussed employing seven perfor-mance evaluation measures i.e. accuracy, precision, recall, F-measure, Re-ceiver Operating Characteristic (ROC) area, Kappa statistic, and Root Mean Squared Error (RMSE). Also, a comprehensive performance analysis has been carried out later in the study.
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Ahmed, F., Samorani, M., Bellinger, C., & Zaiane, O. R. (2016). Advantage of Integration in Big Data: Feature Generation in Multi- Relational Databases for Imbalanced Learning. In Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016 (pp. 532–539).

Biau, G., Devroye, L., & Lugosi, G. (2008). Consistency of Random Forests and Other Averaging Classifiers. Journal of Machine Learning Research, 9, 2015–2033.

Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.

Cao, Y., Miao, Q. G., Liu, J. C., & Gao, L. (2013). Advance and prospects of AdaBoost algorithm. Acta Automatica Sinica, 39(6), 745–758.

Classification via regression wrapper (2019). Retrieved from

Cummings, J., Aisen, P. S., Dubois, B., Frolich, L., Jack, C. R., Jones, R. W., … Scheltens, P. (2016). Drug development in Alzheimer’s disease: The path to 2025. Alzheimer’s Research and Therapy, 8(1), 1–12.

Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198.

Frank, E., Wang, Y., Inglis, S., Holmes, G., & Witten, I. H. (1998). Using Model Trees for Classification. Machine Learning, 32(1), 63–76.

Goldstein, B. A., Navar, A. M., Pencina, M. J., & Ioannidis, J. P. A. (2017). Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. Journal of the American Medical Informatics Association, 24(1), 198–208.

Grassi, M., Rouleaux, N., Caldirola, D., & Loewenstein, D. (2019). A Novel Ens emble-Based Machine Learning Algorithm to Predict the Conversion From Mild Cognitive Impairment to Alzheimer ’ s Disease Using Socio-Demographic Characteristics, Clinical Information, and Neuropsychological Measures, 10(July), 1–15.

Hara, Y. (2018, July 2). How does Alzheimer’s affect women and men differently? Retrieved from

Hinrichs, C., Singh, V., Xu, G., & Johnson, S. C. (2011). Predictive markers for AD in a mult i-modality framework: An analysis of MCI progression in the ADNI population. NeuroImage, 55(2), 574–589.

Ito, K., Corrigan, B., Romero, K., Anziano, R., Neville, J., Stephenson, D., & Lalonde, R. (2013). Understanding placebo responses in Alzheimer’s disease clinical trials from the literature meta-data and CAMD database. Journal of Alzheimer’s Disease, 37(1), 173–183.

Kennedy, R. E., Cutter, G. R., Wang, G., & Schneider, L. S. (2016). Post Hoc Analyses of ApoE Genotype-Defined Subgroups in Clinical Trials. Journal of Alzheimer’s Disease, 50(4), 1205–1215.

Khan, A., & Zubair, S. (2018). Machine Learning Tools and Toolkits in the Exploration of Big Data. International Journal of Computer Sciences and Engineering, 6(12), 570–575.

Khan, A., Zubair, S., & Sabri, M. Al. (2019a). An Improved Pre-processing Machine Learning Approach for Cross-Sectional MR Imaging of Demented Older Adults. In 2019 First International Conference of Intelligent Computing and Engineering (ICOICE) (pp. 1–7). IEEE.

Khan, A., & Zubair, S. (2019b). Usage Of Random Forest Ensemble Classifier Based Imputation And Its Potential In The Diagnosis Of Alzheimer’s Disease. International Journal of Scientific & Technology Research, 8(12), 271–275.

Khan, A., & Zubair, S. (2020a). A Machine Learning-based robust approach to identify Dementia progression employing Dimensionality Reduction in Cross-Sectional MRI data. In 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH), Riyadh, Saudi Arabia (pp. 237–242).

Khan, A., & Zubair, S. (2020b). An Improved Multi-Modal based Machine Learning Approach for the Prognosis of Alzheimer’s Disease. Journal of King Saud University - Computer and Information Sciences.

Khan, A., & Zubair, S. (2020c). Expansion of Regularized Kmeans Discretization Machine Learning Approach in Prognosis of Dementia Progression. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India (pp. 1–6).

Khan, A., & Zubair, S. (2020d). Longitudinal Magnetic Resonance Imaging as a Potential Correlate in the Diagnosis of Alzheimer Disease: Exploratory Data Analysis. JMIR Biomedical Engineering, 5(1), 1–13.

Kohavi, R. (1995). The Power of Decision Tables. In ECML’95: Proceedings of the 8th European Conference on Machine Learning (pp. 174–189).

Lasko, T. A., Denny, J. C., & Levy, M. A. (2013). Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data. PLoS ONE, 8(6).

Medical Research Council (2019). Neurodegeneration, dementia, and mental health. Retrieved from

Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C. R., Jagust, W., … Beckett, L. (2005). Ways toward an early diagnosis in Alzheimer’s disease: The Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimer’s and Dementia, 1(1), 55–66.

Myers, P. D., Scirica, B. M., & Stultz, C. M. (2017). Machine Learning Improves Risk Stratification after Acute Coronary Syndrome. Scientific Reports, 7(1), 1–12.

Random Trees classifier. (2020). Retrieved from

REPTree (2020). Retrieved from

Riedel, B. C., Thompson, P. M., & Brinton, R. D. (2016). Age, APOE and Sex: Triad of Risk of Alzheimer’s Disease. J Steroid Biochem Mol Biol., 134–147.

Risacher, S., Saykin, A., Wes, J., Shen, L., Firpi, H., & McDonald, B. (2009). Baseline MRI Predictors of Conversion from MCI to Probable AD in the ADNI Cohort. Current Alzheimer Research, 6(4), 347–361.

Rogers, J. A., Polhamus, D., Gillespie, W. R., Ito, K., Romero, K., Qiu, R., . Corrigan, B. (2012). Combining patient-level and summary-level data for Alzheimer’s disease modeling and simulation: a beta regression meta-analysis. Journal of Pharmacokinetics and Pharmacodynamics, 39(5), 479–498.

Romero, K., Ito, K., Rogers, J. A., Polhamus, D., Qiu, R., Stephenson, D., . Corrigan, B. (2015). The Future Is Now: Model-Based Clinical Trial Design for Alzheimer’s Disease. Clinical Pharmacology and Therapeutics, 97(3), 210–214.

Ruder, S. (2016). An overview of gradient descent optimization algorithms. Retrieved from

Sonkusare, S. K., Kaul, C. L., & Ramarao, P. (2005). Dementia of Alzheimer’s disease and other neurodegenerative disorders — memantine, a new hope. Pharmacological Research, 51(1), 1–17.

Suk, H. Il, Lee, S. W., & Shen, D. (2014). Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage, 101, 569–582.

Sukel, K. (2018, November 15). Figuring Out Why Alzheimer’s Disease Strikes More Women Than Men. Retrieved from

Szalkai, B., Grolmusz, V. K., & Grolmusz, V. I. (2017). Identifying combinatorial biomarkers by association rule mining in the CAMD Alzheimer’s database. Archives of Gerontology and Geriatrics, 73, 300–307.

Team, E. (2020, May 6). What is Machine Learning? A definition - Expert System. Retrieved from

Tin Kam Ho. (1995). Random Decision Forests. In Proceedings of 3rd International Conference on Document Analysis and Recognition (pp. 278–282). Retrieved from

Venkatesan, E., & Velmurugan, T. (2015). Performance Analysis of Decision Tree Algorithms for Breast Cancer Classification. Indian Journal of Science and Technology, 8(29), 1–8.

Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Green, R. C., . Trojanowski, J. Q. (2013). The Alzheimer’s Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimer’s and Dementia, 9(5), e111–e194.
Khan, A., Zubair, S. ., & Khan, S. (2021). Comprehensive Performance Analysis of Neurodegenerative disease Incidence in the Females of 60-96 year Age Group. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(2).


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