Machine Learning-Driven Internet of Medical Things (ML-IoMT)-Based Healthcare Monitoring System

Kutubuddin Sayyad Liyakat Kazi

2024Advances in healthcare information systems and administration book series36 citationsDOI

Abstract

In order to forecast nine deadly diseases, including blood pressure, diabetes, hepatitis, and kidney disorders, seven machine learning classification algorithms were utilised in this work: adaptive boosting, Random Forest, Decision Trees, Support Vector Machines, Naïve Bayes, Artificial Neural Networks, and K-Nearest Neighbour. Performance criteria such as Accuracy, Precision, and Recall are employed to evaluate the efficacy of the proposed model. Four measures are used to assess the classifiers' performance: accuracy, precision, recall, and precision. The present healthcare model reaches a minimum accuracy of 82.3% and a maximum accuracy of 95.7% for each condition. Every disease has a minimum precision of 81.4% and a maximum precision of 95.7%, as well as a minimum recall of 64.3% and a maximum recall of 90.3%

Topics & Concepts

Naive Bayes classifierPrecision and recallRandom forestRecallMachine learningComputer scienceArtificial intelligenceSupport vector machineDecision treeArtificial neural networkBoosting (machine learning)Data miningLinguisticsPhilosophyNetwork Security and Intrusion DetectionOrganizational and Employee PerformanceInternet of Things and AI