HMLT: Hybrid Machine Learning Technique for Prediction Heart Disease based on Internet of Things
Keywords:
Heart Disease Prediction, Hybrid Machine Learning Technique, Internet of Things, Machine LearningAbstract
It is very vital and alarming to be able to predict heart diseases before it manifest, as the number of people diagnosed with cardiac diseases is rising at an exponential rate day by day. Since determining this diagnosis is a challenging process, it is essential that it be carried out in an accurate and timely manner. The proposed study effort centres mostly on the issue of which patients are more prone to suffer from heart disease depending on a variety of different medical parameters. By using patient's past medical history, a method called the Hybrid Machine Learning Technique (HMLT) that is based on the Internet of Things (IOT) has been presented as an approach for determining whether or not a patient is likely to be identified with a cardiovascular disorders. The performance of the proposed HMLT is compared to that of a number of traditional methods, including Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes (NB), X-GBOOST, Artificial Neural Network (ANN) and Random Forest (RF). The results of the experiments demonstrate that the effectiveness of the HMLT-based cardiac disease prediction system that has been developed is superior to that of other techniques. This conclusion was reached as a result of the findings of the studies. The newly developed methodology indicates that HMLT has a performance accuracy of 96%, which is superior to that of the traditional classification algorithms that are presently in use.
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