Review Paper on Predicting Mood Disorder Risk Using Machine Learning

Afzal Ahmad¹ , Mohammad Asif² , Shaikh Rohan Ali³

Section:Review Paper, Product Type: Isroset-Journal
Vol.7 , Issue.1 , pp.16-22, Feb-2019

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v7i1.1622

Online published on Feb 28, 2019

Copyright © Afzal Ahmad, Mohammad Asif, Shaikh Rohan Ali . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The observe findings additionally accomplice numerous other elements with an elevated chance of developing BD, together with the preterm start, head harm, drug exposures (especially cocaine), physical or sexual abuse, and other kinds of stress. But, for most of those chance factors, each sensitivity and specificity are low. The capability to predict the danger of developing a mood disease for a given individual with sufficient accuracy may be of sizeable help in diagnosing and treating temper disorders earlier than they emerge as a major health issue. In this paper we gave our full attraction on the mood disorder patient and their data history. Our main aim how optimizes this real world problem due to help of the machine learning concept. The prediction of the mood disorder contemporizes through the machine learning. The usage of a facts mixing and system learning method, this thesis seeks to develop and evaluate a prediction model focused across the affected person records this is able to predict the individualized risk for mood disorder development which will facilitate early prognosis in undiagnosed individuals. The focal point of the statistics blending method turned into to observe the relationship between an individual’s personal environment and their mood disorder risk.

Key-Words / Index Term :
Machine learning, Brain Daises, Zone Improvement Plan, Electronic Medical Records

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