MACHINE LEARNING–BASED PREDICTION OF DRUG PRESCRIPTION USING PATIENT PHYSIOLOGICAL AND BIOCHEMICAL CHARACTERISTICS
Abstract
The increasing access to clinical information has provided novel possibilities of using machine learning methods to aid healthcare decision-making. The paper examined how machine learning strategies could be used to forecast the type of drug based on patient physiological and biochemical factors. The reviewed structured clinical dataset involved 200 patient records. The variables comprised age, sex, blood pressure, cholesterol level, sodium-to-potassium ratio (Na to K), and the type of drug that was prescribed. Analytical tools of descriptive and exploratory nature were run to investigate connections between patient characteristics and drug classification trends. The findings showed that the most commonly occurring type of drugs was DrugY (45.5% of the observations), then DrugX (27.0%), DrugA (11.5%), DrugB (8.0%) and DrugC (8.0%). It was found that significant connections existed between drug categories and patient characteristics. Specifically, the blood pressure measured in relation to other drug groups was unanimously correlated with DrugY, although the higher Na to K ratios were more often related to DrugY. Such results indicate that physiological and biochemical variables are significant in distinguishing types of drugs. The study underscores the relevance of machine learning-based methods of analytics in detecting trends in clinical data, which can be used in medication classification
and decision-making. Further studies should use bigger datasets and more clinical variables to enhance predictive modelling and the use of data-driven methods in healthcare analytics.
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