Self-Harm Prediction Model Using Machine Learning Technology

Abstract

Psychological Disorders like self-harm and depression are very common among the people in the
age range of 15-30 years. The host category of this age range is mainly the institute going students as
it affects their lifestyle and the efficiency of the students to perform well on academic fronts. If the
situation persists, they might even commit suicide if they are not diagnosed at an early stage. Machine
learning is a powerful tool for predicting such medical situations. Hence the research focuses on
predicting whether an institute going student shows any self-harm tendencies. The dataset of 353
students was considered and analyzed for predicting the performance of the techniques used. This
research has further applied seven machine learning algorithms and has compared their results on the
dataset collected. Out of the seven, the best working algorithm considered on the dataset is the Random
Forest Algorithm and hence the model was trained on it. In the model, the researcher's has considered
twenty-five attributes out of which it has been reduced to thirteen attributes using random forest
classifier feature importance method. Further using Stratified K Fold on the dataset the research has
sampled the training data. In the end, fine-tuning the hyperparameters using Grid Search CV the
classifier model is trained.