Paper Title
Decision tree analysis based on prediction of bipolar disorder
Article Identifiers
Authors
KALPANA , RJEGADEESAN , E.MADHURI , K.MOUNIKA , G.AJAY, A.SANJAY
Keywords
Decision tree analysis based on prediction of bipolar disorder
Abstract
This study investigates the use of choice tree analysis in bipolar illness prediction. In statistics analysis, decision trees are an effective tool for identifying patterns and formulating predictions that are mostly dependent on input variables. We investigate various elements in this study, including mood swings, the length of mood episodes, family histories, and additional symptoms and indicators associated with bipolar disorder. Our goal is to broaden the application of a straightforward yet effective method for estimating a person's risk of developing bipolar dis order by examining these variables using selection tree approaches. In the end, improving patient outcomes and quality of life, the study's findings should contribute to early diagnosis and intervention strategies for this intellectual health problem. Because bipolar illness is a complicated mental health condition marked by recurrent bouts of mania and depression, diagnosing it can be difficult. Effective intervention and management of bipolar disorder depend on early diagnosis. Because decision tree analysis can handle complex datasets and provide interpretable models, it has emerged as a viable tool for bipolar illness prediction. Decision trees provide a hierarchical structure that aids in comprehension by recursively splitting data based on important qualities, simulating human decision-making processes. This openness is especially helpful for diagnosing mental health issues, when readability and clarity are crucial. Integration of several data types, such as clinical symptoms, genetic markers, environmental impacts, and demographic characteristics, is possible with decision tree analysis. Decision trees facilitate the creation of precise predictive models that can identify persons at risk of bipolar disorder by identifying relevant factors and their interactions.
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How To Cite (APA)
KALPANA, RJEGADEESAN, E.MADHURI, K.MOUNIKA, & G.AJAY, A.SANJAY (May-2024). Decision tree analysis based on prediction of bipolar disorder. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), h74-h83. https://ijnrd.org/papers/IJNRD2405727.pdf
Issue
Volume 9 Issue 5, May-2024
Pages : h74-h83
Other Publication Details
Paper Reg. ID: IJNRD_222567
Published Paper Id: IJNRD2405727
Downloads: 000121983
Research Area: Engineering
Country: -, -, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2405727.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405727
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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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