International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Suicide Rate Prediction Using Machine Learning

Author(s) Kanishka Khatavkar, Dineshkumar P, Vaishnavi Phalke, Vaishnavi Bhosale, Akshada Ghorpade
Country India
Abstract Suicide is a growing public health concern with accurate prediction and prevention being crucial for saving lives. This project utilized advanced machine learning algorithms to predict suicide rates using a dataset of demographic, economic, mental-health and social factors. Our approach employs a combination of regression models, decision trees, Random Forest, multilayered perception to identify key predictors and patterns in suicide rates. Results show that our machine learning model achieves a high accuracy rate in predicting suicide rates, outperforming traditional statistical methods. Feature importance analysis reveals that economic factors, such as unemployment rates and income levels, are significant predictors of suicide rates. These findings have important implications for policy makers and mental health professionals, enabling targeted interventions and resource allocation to high-risk populations. Our study demonstrates the potential of machine learning in improving suicide prevention of machine learning in improving suicide prevention efforts and highlights the need for further research in this critical area.
Keywords Suicide Rate Prediction, Machine Learning, Supervised Learning, Mental health services, public health interventions.
Field Engineering
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-01-10
Cite This Suicide Rate Prediction Using Machine Learning - Kanishka Khatavkar, Dineshkumar P, Vaishnavi Phalke, Vaishnavi Bhosale, Akshada Ghorpade - IJFMR Volume 7, Issue 1, January-February 2025.
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E-ISSN 2582-2160
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