Bipolar Disorder Detection using Machine Learning

Bala Sakthi Ganesh M; Jagatheeshwaran S; Apurva S V

doi:10.36948/ijfmr.2023.v05i03.2932

Bipolar Disorder Detection using Machine Learning

Author(s)	Bala Sakthi Ganesh M, Jagatheeshwaran S, Apurva S V
Country	India
Abstract	Bipolar disorder, also known as manic-depressive illness, is a mental health condition that affects a person's mood, energy, and ability to function. People with bipolar disorder experience extreme shifts in their mood, ranging from periods of high energy and elation (called mania or hypomania) to periods of deep depression. This can interfere with a person's ability to work, study, or have healthy relationships. The diagnosis of bipolar disorder can be challenging due to the variability of symptoms and the lack of objective diagnostic tests. Machine learning algorithms have shown great potential in aiding the diagnosis of bipolar disorder by analyzing patterns in large datasets of clinical and neuroimaging data. In this paper, we present a machine learning approach for the detection of bipolar disorder using clinical and neuroimaging data. We applied feature selection and machine learning algorithms to classify patients with bipolar disorder from healthy controls. Our results showed that the proposed machine learning approach achieved an accuracy of 85% in classifying patients with bipolar disorder from healthy controls using clinical and neuroimaging data. These results suggest that machine learning can aid in the detection of bipolar disorder and may provide an objective diagnostic tool for clinicians.
Keywords	manic-depressive illness, deep depression, diadnosis, neuroimaging data, classifying patients, diagnostic data
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 5, Issue 3, May-June 2023
Published On	2023-05-10
Cite This	Bipolar Disorder Detection using Machine Learning - Bala Sakthi Ganesh M, Jagatheeshwaran S, Apurva S V - IJFMR Volume 5, Issue 3, May-June 2023. DOI 10.36948/ijfmr.2023.v05i03.2932
DOI	https://doi.org/10.36948/ijfmr.2023.v05i03.2932
Short DOI	https://doi.org/gr77p9

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 4 Cover Page Vol 6 Isu 3 Cover Page Vol 6 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Bipolar Disorder Detection using Machine Learning

Share this