An Intelligent Healthcare System for Automated Alzheimer’s Disease Prediction and Personalized Care

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Published Apr 1, 2021
https://doi.org/10.47164/ijngc.v12i2.196
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Volume 12, Special Issue 2, April 2021

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Tawseef Ayoub Shaikh
Baba Ghulam Shah Badshah University, Rajouri
WASEEM AHMAD MIR
Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh 202002, India
Izharuddin Mohammad
Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh 202002, India
Rashid Ali

Abstract

Alzheimer’s disease has posed the greatest threat among all the different types of neurodegenerative problems as it has assaulted humankind at quick pace than the others. Its manual revelation has become clinically insignificant because of the in expertise, high rate of false positives (FP) and false-negatives (FN). To reduce the false positive/ false negative rate, this paper frames a quick, affordable, and objective judgement of AD with a novel data mining method coalescing a global Maximum Relevance and Minimum Redundancy (MRMR) based filter heuristic with a globally optimised wrapper heuristic GANNIGMA with the intention of minimalising the consequence of an imbalanced healthcare dataset. The optimal feature subset yielding the best performance are utilised for model training of Decision Tree, k-NN, and SVM algorithms. The trial results on benchmark ADNI dataset using the proposed model displayed the Decision Tree attains TP rate of 0.778, and AUC of 0.798, k-NN acquires 0.764 TP Rate and 0.784 AUC, and SVM attains 0.997 TP Rate, and 0.996 as AUC. The results are far healthier than the separate results of these algorithms attained on the same dataset with fewer optimal feature subsets.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Tawseef Ayoub Shaikh, WASEEM AHMAD MIR, Izharuddin Mohammad, & Rashid Ali. (2021). An Intelligent Healthcare System for Automated Alzheimer’s Disease Prediction and Personalized Care. International Journal of Next-Generation Computing, 12(2), 240–253. https://doi.org/10.47164/ijngc.v12i2.196
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