MACHINE LEARNING SYSTEM FOR CATEGORIZATION OF DRUGS WITH ADVERSE EFFECT BASED ON NAIVE BAYES AND XGBOOST ALGORITHM

The biological and hormonal variables associated with gender can affect the absorption and metabolism of medications within the body. Thus, a thorough examination of ADE requires an understanding of the age and gender distribution in the sample. It is common for symptoms like headache, nausea, sneezing, and nasal congestion to accompany adverse pharmaceutical responses. Since these symptoms have a significant impact on a patient's health and adherence to their treatment plan, it is imperative to monitor them. The collection also contains data on the severity of these side effects following drug delivery, which sheds light on how serious the side effects are. Healthcare providers can use this information to customize treatment regimens and reduce the possibility of negative reactions. The severity of symptoms after medication is strongly correlated with the following features: Worst Nasal Congestion After Drug, Excessive Sneezing After Drug, Intensified Headache After Drug, and Worst Nausea After Drug. Healthcare professionals must comprehend these factors in order to evaluate the safety and tolerability of particular medications. The collection also contains details on underlying medical conditions and Body Mass Index (BMI), which might affect a drug's metabolism and susceptibility to negative side effects. Together, these characteristics can help medical practitioners better anticipate and handle possible negative drug reactions, which will improve patient safety and treatment results.