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

TL;DR

This study explores machine learning methods, especially Random Forest, to improve Alzheimer's diagnosis accuracy while reducing the number of required medical tests, achieving 94% detection accuracy with fewer indicators.

Contribution

It demonstrates how to fine-tune classifiers to maintain high accuracy with fewer tests, optimizing the diagnostic process for Alzheimer's disease.

Findings

Achieved 94% detection accuracy using only four indicators.

Random Forest was identified as the most effective classifier.

Reduced the number of tests needed for diagnosis.

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.