Leveraging Machine Learning for Early Detection and Prediction of Suicidal Tendencies in Women

Suicide is a major public health issue, with women showing significant risk in various demographic and psychological contexts. Despite the availability of clinical tools to assess suicide risk, early and accurate prediction remains challenging. The major aim of this paper is a study that will help females in recognizing their suicidal tendencies; women will be capable of predicting the tendencies using the clinical, behavioral, and demographic data. For this purpose, quite a number of supervised machine learning techniques like random forests, logistic regression, and neural networks have been employed so as to classify the most critical factors underlying the identification of attempting suicide. The results highlight the potential of ML in providing predictive insights, emphasizing the need for ethical data handling, model interpretability, and integration with mental health support systems. Our findings suggest that machine learning models can serve as an adjunct tool for early intervention and targeted care.