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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	arXiv (Machine Learning)
Publisher	Cornell University
Pages	1-10
Number of pages	10
Publication status	Accepted/In press - 9 Nov 2022
Event	3rd International Conference on Machine Learning Techniques and Data Science (MLDS 2022) - London, United Kingdom Duration: 26 Nov 2022 → 27 Nov 2022 https://iccsea2022.org/mlds/papers

Conference

Conference	3rd International Conference on Machine Learning Techniques and Data Science (MLDS 2022)
Country/Territory	United Kingdom
City	London
Period	26/11/22 → 27/11/22
Internet address	https://iccsea2022.org/mlds/papers

Keywords

Machine Learning
Accuracy
precision
recall
Random Forest
Alzheimer’s disease

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Md Sharifur Rahman_MLDS 2022_CSCP format_latestAuthor Accepted Manuscript, 954 KBLicence: CC BY-NC-SA

https://arxiv.org/abs/2212.00414

Cite this

@inproceedings{e3f0f67af9874672bd627048f3fa5fc9,

title = "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",

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.",

keywords = "Machine Learning, Accuracy, precision, recall, Random Forest, Alzheimer{\textquoteright}s disease",

author = "Rahman, {Md. Sharifur} and Girijesh Prasad",

year = "2022",

month = nov,

day = "9",

language = "English",

pages = "1--10",

booktitle = "arXiv (Machine Learning)",

publisher = "Cornell University",

address = "United States",

note = "3rd International Conference on Machine Learning Techniques and Data Science (MLDS 2022) ; Conference date: 26-11-2022 Through 27-11-2022",

url = "https://iccsea2022.org/mlds/papers",

}

Rahman, MS & Prasad, G 2022, 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. in arXiv (Machine Learning). Cornell University, pp. 1-10, 3rd International Conference on Machine Learning Techniques and Data Science (MLDS 2022), London, United Kingdom, 26/11/22. <https://arxiv.org/abs/2212.00414>

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. / Rahman, Md. Sharifur ; Prasad, Girijesh.
arXiv (Machine Learning). Cornell University, 2022. p. 1-10.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - 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

AU - Rahman, Md. Sharifur

AU - Prasad, Girijesh

PY - 2022/11/9

Y1 - 2022/11/9

N2 - 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.

AB - 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.

KW - Machine Learning

KW - Accuracy

KW - precision

KW - recall

KW - Random Forest

KW - Alzheimer’s disease

M3 - Conference contribution

SP - 1

EP - 10

BT - arXiv (Machine Learning)

PB - Cornell University

T2 - 3rd International Conference on Machine Learning Techniques and Data Science (MLDS 2022)

Y2 - 26 November 2022 through 27 November 2022

ER -

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

Abstract

Conference

Keywords

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Cite this