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

Document detail

ID

oai:arXiv.org:2212.00414

Topic

Computer Science - Machine Learnin... Computer Science - Artificial Inte...

Author

Rahman, Md. Sharifur Prasad, Professor Girijesh

Year

2022

listing date

12/7/2022

Keywords

alzheimer disease diagnose tests

Metrics

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.

;Comment: Presented at the 3rd International Conference on Machine Learning Techniques and Data Science (MLDS 2022)

Rahman, Md. Sharifur,Prasad, Professor Girijesh, 2022, 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