Characterizing the critical features when personalizing antihypertensive drugs using spectrum analysis and machine learning methods

Abstract

Globally, methods of controlling blood pressure in hypertension patients remain inefficient. The difficulty of prescribing appropriate drugs specific to a patient's clinical features serves as one of the most important factors. Characterizing the critical drug-related features, just like that of the antibacterial spectrum (where each item is sensitive to the targeted drug's effectiveness or a specified indication), may help a doctor easily prescribe appropriate drugs by matching a patient's attributes with drug-related features, and effectiveness of the selected drugs would also be ascertained. In this study, we aimed to apply data mining methods to obtain the clinical characteristics spectrum or important clinical features of five frequently used drugs (Irbesartan, Metoprolol, Felodipine, Amlodipine, and Levamlodipine) for hypertension control by comparing successful and unsuccessful cases. Spectrum analysis based on a statistical method and five algorithms based on machine learning were used to extract the critical clinical features. A visualized relative weight matrix was then achieved by combining the results from the characteristic spectrum and machine learning-based methods. Our results indicated that the five targeted antihypertension agents had different importance orders of the 15 relative clinical features. Clinical analysis showed that the extracted important clinical attributes of the five drugs were both reasonable and meaningful in the selection of hypertension treatment. Therefore, our study provided a data-driven reference for the personalization of clinical antihypertensive drugs.

Keywords: Antihypertensive drugs; Blood pressure control; Data mining methods; Drug-related attributes; Machine learning.