DALY (Disability-Adjusted Life Year) is a measure that quantifies the burden of disease, combining years of healthy life lost due to premature death and disability. MindFit-AI utilizes machine learning techniques, specifically Random Forest regression, to predict DALY percentages for mental health disorders. By providing accurate predictions and insights, MindFit-AI empowers evidence-based decision-making, resource allocation, and support strategies, contributing to overall well-being.

MindFit-AI is an open-source project aimed at analyzing and predicting DALY percentages for a comprehensive range of mental health disorders. Leveraging datasets from reputable sources like Our World in Data, the project utilizes machine learning techniques, specifically Random Forest Regression from Scikit-Learn, to provide accurate predictions. The covered disorders include:

• Schizophrenia
• Bipolar disorders
• Eating disorders
• Anxiety disorders
• Drug use disorders
• Depressive disorders
• Alcohol use disorders

MindFit-AI offers personalized insights, aids in resource allocation and decision-making, and fosters transparency and collaboration as a FOSS (Free and Open Source Software) project hosted on GitHub.

Mental health disorders are a prevalent global concern, affecting millions of individuals worldwide. Existing methods often struggle to provide accurate predictions of the impact of these disorders on DALY percentages, hindering effective resource allocation and support strategies. There is a critical need for data-driven insights to better understand, predict, and address the impact of mental health disorders. MindFit-AI aims to address these challenges by offering accurate predictions and insights on DALY percentages, empowering informed decision-making, and improved support strategies.

The project is based on the following datasets:

1. Prevalence by Mental and Substance Use Disorder: This dataset provides information on the prevalence of mental and substance use disorders across different countries and time periods.

2. Mental and Substance Use as Share of Disease: This dataset offers insights into the share of mental and substance use disorders as a proportion of the overall disease burden.

Please refer to the respective dataset links for more details and usage guidelines.

We extend our sincere appreciation to the collaborative efforts of the Edunet Foundation, IBM SkillsBuild team, and DIEMS (Deogiri Institute of Engineering and Management Studies, Aurangabad). The Edunet Foundation provided dedicated teachers, while IBM SkillsBuild contributed valuable study materials. The success of this project highlights the power of collaboration in addressing critical challenges in mental health.

For the latest version of MindFit-AI and detailed release notes, please visit the Release Page. Stay up-to-date with the latest improvements and features.