Mental Health Harmony: Insights from the Machine Learning Frontier

Detecting mental health problems at the earliest can help professionals provide correct treatment and increase the value of patients. One of the most prevalent types of impairment in the world is depression. It is important to deal adolescent mental health issues which can escalate to more serious problems if not addressed early. Machine learning techniques are ideal for estimating and diagnosing medical data. Feature selection techniques have been applied to the entire attribute dataset to reduce attributes. Machine Learning accuracy is associated with attributes from the dataset. Three algorithms are employed in this work, Logistic Regression, Random Forest Classifiers, and Decision Tree Classifiers, in data science techniques. Sensitivity and Specificity are measures that estimate a model’s capability to predict true positive and negative results. When these algorithms are compared the random forest classifier yields 95% accuracy when compared with other algorithms.