Abstract:
Indication to World Health Organization, suicide is a major world public health concern that is in the top twenty leading causes of death worldwide. Sri Lanka is a country that has the highest suicidal rates in the globe. The comprehensive study about risk factors for suicide is important because we can prevent or treat the recognized most risky categories of people. The emergence of big data concepts with machine learning techniques introduced a resurgence in predicting models using risk factors for suicide. Regularization is one of the most decisive components in the statistical machine learning process and this technique is used to reduce the error on the training dataset and prevent over-fitting. Comprehensive regularization approaches are presented here to select significant risk factors for age-specific suicide in Sri Lanka and build unique predictive models. The Least Absolute Shrinkage and Selection Operator (LASSO) approach presents regularization along with the feature selection to improve the prediction precision. The dataset collected for the study is rooted in the Sri Lankan people and the factors used for the analysis are, suicide person’s gender, lived place, education level, mode of suicide, job, reason, suicide time, previous attempts, and marital status. Further, the riskiest age category of the people, who has exposure to suicide, is identified. Multiple linear regression and Ridge regression were used to evaluate the performance of LASSO. The selected most relevant factors with regularization to predict age-specific suicide prove the effectiveness of the proposed regularization approaches.
