Mental Health Prediction Using Machine Learning

Machine learning techniques have transformed healthcare by enabling precise and timely disease prediction. The capacity to forecast multiple diseases simultaneously can greatly enhance early diagnosis and treatment, leading to improved patient outcomes and lower healthcare expenses. This research paper delves into the use of machine learning algorithms for predicting various diseases, highlighting their advantages, challenges, and prospects. It provides a comprehensive overview of different machine learning models and the data sources frequently employed in disease prediction. Furthermore, it emphasises the importance of feature selection, model evaluation, and the integration of diverse data types to improve prediction accuracy. The findings underscore the significant potential of machine learning in predicting multiple diseases and its impact on public health. Specifically, the study demonstrates the application of a machine learning model to determine if an individual is affected by certain diseases. This model is trained using sample data to enhance its predictive capabilities.