“Enhancing Mental Health Assessments: The Role of Voting Classifiers in Evaluating Depression's Impact on Quality of Life” | [International Journal of Communication Networks and Information Security • 2024]

Author(s):

1. S.Pavani: Department of CS,CMDubey PG College,Bilaspur,India

2. Kajal Kiran Gulhare: Department of CS,Govt.ERagawendra Rao Science College,Bilaspur, India.

3. Richa Handa: D.P.Vipra college,Department of CS ,Bilaspur, India

4. Sunita Kushwaha: Department of CS, MATS University,Raipur,India

Abstract:

Depression continues to pose a significant global challenge, ranking as one of the most prevalent and costly mental disorders that substantially impairs quality of life, supported by a substantial body of research. Enhancing our comprehension of the factors influencing quality of life is paramount for optimizing long-term outcomes and reducing disability in individuals grappling with depression. This study primarily focuses on the identification of depression based on lifestyle and livelihood factors. It's noteworthy that depression can afflict individuals across all age groups, genders, and backgrounds, often arising from a complex interplay of genetic, biological, environmental, and psychological elements. Furthermore, major life events, chronic stress, trauma, or a family history of depression can contribute to its emergence. In the realm of healthcare, machine learning techniques are increasingly employed to process and analyze diverse data types, with the aim of better understanding the relationship between quality of life factors and depression. Various classification algorithms, such as Random Forest, Decision Tree, Naive Bayes, Support Vector Machine, and PPMCSVM, have been utilized for this analysis. However, existing approaches have encountered challenges related to their accuracy in predicting depression. Consequently, the primary objective of this proposed research is to enhance depression prediction by leveraging an ensemble technique that identifies the determinants of quality of life among individuals affected by depression. To attain this goal, the study employs KNN (K-Nearest Neighbour) and Voting Classifier algorithms. The Voting Classifier aids in uncovering the root causes of depression in each individual. The results of this investigation reveal that the proposed model can effectively predict the causes of depression, thus opening avenues for more targeted intervention and treatment strategies

Page(s): 727-735

DOI: DOI not available

Published: Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 4, Year: 2024

Keywords:

Depression , Intervention , Voting Classifier , Ensemble Technique , Mental Disorder , healthcare system , Treatment Strategies , prediction accuracy , underlying causes , PPMCSVM

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