A Comprehensive Study on Machine Learning Methods to Increase the Prediction Accuracy of Classifiers and Reduce the Number of Medical Tests Required to Diagnose Alzheimer'S Disease

• URL: http://arxiv.org/abs/2212.00414v1
• Date: Thu, 1 Dec 2022 10:34:11 GMT
• Title: A Comprehensive Study on Machine Learning Methods to Increase the Prediction Accuracy of Classifiers and Reduce the Number of Medical Tests Required to Diagnose Alzheimer'S Disease
• Authors: Md. Sharifur Rahman, Professor Girijesh Prasad
• Abstract summary: The main goal of this study is to fine-tune the classifier to detect illness with fewer tests while maintaining a reasonable disease discovery accuracy. We successfully identified the condition in almost 94% of cases using four of the thirty frequently utilized indicators.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Alzheimer's patients gradually lose their ability to think, behave, and interact with others. Medical history, laboratory tests, daily activities, and personality changes can all be used to diagnose the disorder. A series of time-consuming and expensive tests are used to diagnose the illness. The most effective way to identify Alzheimer's disease is using a Random-forest classifier in this study, along with various other Machine Learning techniques. The main goal of this study is to fine-tune the classifier to detect illness with fewer tests while maintaining a reasonable disease discovery accuracy. We successfully identified the condition in almost 94% of cases using four of the thirty frequently utilized indicators.

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