Description of Invention
The study uses machine learning to analyze suicide rates, aiming to create predictive models that enhance our understanding of suicide risk factors. Utilizing a diverse dataset including variables like age, sex, occupation, and method of suicide, the research applies advanced classification algorithms within the CRISP-DM framework. Key findings indicate that the method of suicide is the most critical determinant of fatality, with lethal means like hanging or firearms increasing the risk. Demographic factors, such as older age and male gender, along with socioeconomic factors like unemployment and marital status, also significantly influence the likelihood of fatal outcomes.