Precision Medicine Through Support Vector Machines Analyzing Patient Data for Improved Drug Classification
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Abstract
Selecting the appropriate medication is crucial for ensuring optimal therapeutic outcomes and minimizing adverse effects for patients. Healthcare personnel are managing an increasing volume of medical data in the digital era. Identifying swift, precise, and dependable methods for recommending appropriate medications is becoming essential. This study aims to meet this criterion by classifying drugs into appropriate categories for patient care using the Support Vector Machine (SVM) technology. The research utilized a dataset from GitHub comprising 200 patient records. These records furnish critical information regarding the patient, including age, sex, blood pressure, cholesterol levels, sodium-to-potassium ratios, and prescriptions. To maximize the use of this data, the method entails several critical steps: selecting appropriate data, meticulously cleaning and organizing it, transforming it for analytical readiness, employing SVM for data mining, and conducting a comprehensive review. The dataset is divided into two segments which are 20% is allocated for testing the efficacy of the SVM model, while the remaining 80% is designated for training the model.The primary tool for constructing the SVM model is the Google Colaboratory platform, which utilizes Python. A confusion matrix is employed to meticulously evaluate the performance of a model. It provides valuable metrics such as accuracy, precision, recall, and the F1 score. The evaluation method indicates that the SVM model holds significant potential for systematically assessing patient data due to its capability to appropriately categorize various drug types. This discovery represents a significant advancement for AI in healthcare, as it facilitates the prompt and straightforward recommendation of individualized medicines by physicians.
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References
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