Article
MIND PREDICTOR: MENTAL HEALTH STATUS CLASSIFICATIONUSINGSUPERVISEDMACHINE LEARNINGALGORITHMS
Mental health disorders such as depression, anxiety, and stress have become significant global concerns due to rapid lifestyle changes, increased social pressure, and technological dependency. Early detection of such conditions remains challenging because many individuals hesitate to seek professional help or remain unaware of their mental health status. This project presents a machine learning-based system, MindPredictor, designed to classify mental health conditions using supervised learning algorithms by analyzing user-generated textual data, particularly from social media platforms. The system leverages natural language processing techniques, including text preprocessing, tokenization, stemming, lemmatization, and sentiment analysis, to extract meaningful insights from user input. Sentiment polarity and subjectivity scores are computed and used as features for classification. The processed data is then fed into machine learning models such as Naïve Bayes and hybrid classifiers to distinguish between depressive and non-depressive states. The system evaluates model performance using metrics like accuracy, precision, recall, and confusion matrix analysis to ensure reliable predictions. Results demonstrate that machine learning algorithms can effectively identify patterns related to mental health conditions and provide accurate classification outcomes. The proposed system offers a cost-effective, scalable, and accessible solution that can assist in early mental health assessment. It does not replace professional diagnosis but acts as a supportive tool for awareness and preliminary screening. By integrating artificial intelligence with healthcare, the system contributes to improved mental health monitoring, timely intervention, and reduced social stigma associated with psychological disorders.
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