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

Md. Sharifur Rahman, Girijesh Prasad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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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.
Original languageEnglish
Title of host publicationarXiv (Machine Learning)
PublisherCornell University
Pages1-10
Number of pages10
Publication statusAccepted/In press - 9 Nov 2022
Event3rd International Conference on Machine Learning Techniques and Data Science (MLDS 2022) - London, United Kingdom
Duration: 26 Nov 202227 Nov 2022
https://iccsea2022.org/mlds/papers

Conference

Conference3rd International Conference on Machine Learning Techniques and Data Science (MLDS 2022)
Country/TerritoryUnited Kingdom
CityLondon
Period26/11/2227/11/22
Internet address

Keywords

  • Machine Learning
  • Accuracy
  • precision
  • recall
  • Random Forest
  • Alzheimer’s disease

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