A Machine Learning Approach to Drug Performance Evaluation: Ensemble Technique
Sajo Sam, Shelly Shiju George · Zenodo (CERN European Organization for Nuclear Research) · 2023
Abstract: This research paper presents an evaluation of drug performance using data from Kaggle with columns such as condition, drug, ease of use, price, type, form, indication, satisfaction, reviews, and efficiency. The study utilizes a combination of four machine learning models: linear regression, random forest, xgboost, and decision tree, to predict drug performance with an accuracy of 87%. The results of this study demonstrate the effectiveness of combining different machine learning algorithms to improve drug performance prediction. This research paper presents a thorough examination of the drug performance evaluation procedure, encompassing the entirety of the process from data gathering to the development and assessment of models. The results of this investigation may be leveraged by healthcare providers to enhance medication management strategies for their clients.