“Enhancing Mental Health Assessments: The Role of Voting Classifiers in Evaluating Depression's Impact on Quality of Life”

Abstract

Depression continues to pose a significant global challenge,ranking as one of the most prevalent and costly mental disordersthat substantially impairs quality of life, supported by asubstantial body of research. Enhancing our comprehension ofthe factors influencing quality of life is paramount for optimizinglong-term outcomes and reducing disability in individualsgrappling with depression. This study primarily focuses on theidentification of depression based on lifestyle and livelihoodfactors. It's noteworthy that depression can afflict individualsacross all age groups, genders, and backgrounds, often arisingfrom a complex interplay of genetic, biological, environmental,and psychological elements. Furthermore, major life events,chronic stress, trauma, or a family history of depression cancontribute to its emergence.In the realm of healthcare, machine learning techniques areincreasingly employed to process and analyze diverse data types,with the aim of better understanding the relationship betweenquality of life factors and depression. Various classificationalgorithms, such as Random Forest, Decision Tree, Naive Bayes,Support Vector Machine, and PPMCSVM, have been utilized forthis analysis. However, existing approaches have encounteredchallenges related to their accuracy in predicting depression.Consequently, the primary objective of this proposed research isto enhance depression prediction by leveraging an ensembletechnique that identifies the determinants of quality of lifeamong individuals affected by depression. To attain this goal, thestudy employs KNN (K-Nearest Neighbour) and Voting Classifieralgorithms. The Voting Classifier aids in uncovering the rootcauses of depression in each individual. The results of thisinvestigation reveal that the proposed model can effectivelypredict the causes of depression, thus opening avenues for moretargeted intervention and treatment strategies.