Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An Empirical and Statistical Analysis of Regression Algorithms Used for Mental Fitness Prediction Subject The topic of the resource bipolar disorder; Gradient Boosting; K-Nearest Neighbors (KNN); Linear Regression; Random Forest; schizophrenia; Support Vector Regression (SVR); Theil Sen Regression Description An account of the resource In today's focus on mental well-being, technology's capability to predict and comprehend mental fitness holds substantial significance. This study delves into the relationship between mental health indicators and mental fitness levels through diverse machine learning algorithms. Drawing from a vast dataset spanning countries and years, the research unveils concealed patterns shaping mental well-being. Precise analysis of key mental health conditions reveals their prevalence and interactions across demographics. Enriched by insights into Disability-Adjusted Life Years (DALYs), the dataset offers a comprehensive view of mental health's broader impact. Through rigorous comparative analysis, algorithms like Linear Regression, Random Forest, Support Vector Regression, Gradient Boosting, K-nearest neighbors and Theil Sen Regression are assessed for predictive accuracy. Mean squared error (MSE), root mean squared error (RMSE), and Rsquared (R2) scores are used to assess the predictive accuracy of each algorithm. Results show that Mean Squared Error (MSE) ranged from 0.030 to 1.277, Root Mean Squared Error (RMSE) from 0.236 to 1.130, and R-squared (R2) scores ranged between 0.734 and 0.993, with Random Forest Regressor achieving the highest accuracy. This study offers precise prognostications regarding mental fitness and establishes the underpinnings for the creation of effective tracking tools. Amidst society's endeavor to tackle intricate issues surrounding mental health, our research facilitates well-informed interventions and individualized strategies. This underscores the noteworthy contribution of technology in shaping a more Invigorating trajectory for the future. 2023 IEEE. Creator An entity primarily responsible for making the resource Jamadagni V.; Vedavalli D.; Priyadarshi S.; Vasudev S.; Prathap B.R. Source A related resource from which the described resource is derived Proceedings - 4th IEEE 2023 International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023, pp. 805-810. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCCIS60361.2023.10425084" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCCIS60361.2023.10425084</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186500666&amp;doi=10.1109%2FICCCIS60361.2023.10425084&amp;partnerID=40&amp;md5=d6ed9ec084972e2f3f1bbb3a4563ee85" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186500666&amp;doi=10.1109%2fICCCIS60361.2023.10425084&amp;partnerID=40&amp;md5=d6ed9ec084972e2f3f1bbb3a4563ee85</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835030611-8 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jamadagni V., PES University, Computer Science and Engineering, Bengaluru, India; Vedavalli D., PES University, Computer Science and Engineering, Bengaluru, India; Priyadarshi S., PES University, Computer Science and Engineering, Bengaluru, India; Vasudev S., PES University, Computer Science and Engineering, Bengaluru, India; Prathap B.R., Christ University, Computer Science and Engineering, Bengaluru, India