An Empirical and Statistical Analysis of Regression Algorithms Used for Mental Fitness Prediction
- Title
- An Empirical and Statistical Analysis of Regression Algorithms Used for Mental Fitness Prediction
- Creator
- Jamadagni V.; Vedavalli D.; Priyadarshi S.; Vasudev S.; Prathap B.R.
- Description
- 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.
- Source
- Proceedings - 4th IEEE 2023 International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023, pp. 805-810.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- bipolar disorder; Gradient Boosting; K-Nearest Neighbors (KNN); Linear Regression; Random Forest; schizophrenia; Support Vector Regression (SVR); Theil Sen Regression
- Coverage
- 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
- Rights
- Restricted Access
- Relation
- ISBN: 979-835030611-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Jamadagni V.; Vedavalli D.; Priyadarshi S.; Vasudev S.; Prathap B.R., “An Empirical and Statistical Analysis of Regression Algorithms Used for Mental Fitness Prediction,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19718.