Intelligent Drug Release System Development and Evaluation Via Support Vector Machine Algorithm

This study proposes a unique sensor plus machine learning system designed to enhance patient care and targeted medicine administration, with a specific focus on controlling Cryptococcosis, in the search for more precise and proactive healthcare solutions. This research had contributed to the conceptualization, design and implementation of system which integrates constant sensor data monitoring and machine learning algorithms to anticipate and prepare for any health problems. Over the course of a day, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Random Forest (RF) were used to analyse sensor data from 10 patients with Cryptococcosis. The sensor data gathered from these people included information on their body temperature, blood pressure, oxygen saturation, heart rate, blood sugar levels, and ECG measurements. The models executed flawlessly, achieving F1 scores for accuracy, recall, and precision. SVM won with a precision score of 97.6%, followed by ANN with a score of 95.3% and RF with a score of 92.1%. The models' capacity to get rid of misclassifications and provide precise predictions needed for right away action was demonstrated by the confusion matrices that went along with them. This discovery, which might revolutionise how patients are treated for difficult-to-treat illnesses like cryptococcosis, is an encouraging first step in the direction of proactive healthcare management. The sensor and machine learning system offers a patient-centered, data-driven approach that keeps up with the changing landscape of healthcare.