A novel behavioral health care dataset creation from multiple drug review datasets and drugs prescription using EDA

Abstract In the current age, the mental condition of people varies rapidly due to various factors such as social relationships, eating disorders, and economic crisis. There are various factors that can be overcome, such as regular exercise, community engagement, meditation, reading books, and yoga. But there are situations where people cannot undergo the mentioned strategies. As advances in data science and big data continue, there is an increasing availability of drug review datasets. There are various manual and traditional approaches to identify the proper drug and condition with some flaws such as overtime, measurement error of the drug, and high computational complexity. Due to these barriers, cutting-edge technologies are involved in data exploration (data cleaning, data transformation, data integration, etc.) to identify the proper prescription of the condition with machine learning approaches. Furthermore, the proposed work has a threefold unique approach that includes the integration of datasets, the creation of a new dataset, and the focus on exploratory data analysis. In the final step, a novel dataset is created from multiple datasets on behavioral healthcare drug reviews that are compared with individual datasets. The main objective of the work is to satisfy the customer’s health in all aspects. The work is verified by identifying the prescription for popular health conditions such as anxiety, depression, insomnia, panic disorder, and bipolar disorder.