Drug Classification Using Machine Learning Algorithms

Personalized medicine is based on the categorization of medications based on each patient’s unique needs. But traditional approaches have drawbacks, especially when it comes to scaling and confidentiality. In this work, we present a new machine learning (ML) method to improve and automate processes related to medicine classification. The study’s dataset came from the open repository Kaggle. The objective point is the kind of medication, and the point settings are coitus, age, BP, cholesterol, and the ratio of sodium to potassium. As part of our process, we replace drug names with placeholders such as DrugX, DrugY, etc. to anonymize them. The patient’s age, sex, blood pressure, cholesterol, and blood salt-to-potassium ratio are also considered. Our approach is evaluated on benchmark datasets to demonstrate its efficacy. Using state-of-the-art machine learning techniques like random forests and neural networks, we extract numerous features from pharmaceutical substances. The results are competitive and demonstrate improved performance over conventional techniques.