IoT Wearable Medical Device for Heart Disease Recognition Based Ml and Dl: A Bovw Based MDCNN Classification Approach

Backgrounnd: Early detection and treatment of cardiac disease can improve the prognosis globally. However, the necessary vast data poses a challenge to the current automatic heart disease diagnosis methods. In this research, a medical gadget that uses the Internet of Things to gather patient cardiac data both before and after heart disease is introduced. Accurate and precise classification of health data is essential for the timely diagnosis of cardiac diseases. The Modified Deep Convolutional Neural Network (MDCNN), a very powerful technique for categorizing medical data, especially in hospitalized patients, was utilized in this work. We applied the Bag of Visual Words (BOVW) algorithm to improve feature selection, and our experimental studies showed a notable increase in classification accuracy. The outcomes verified that the suggested BOVW-based MDCNN model performed remarkably well in predicting cardiac diseases, with a limit exactness of 98.77% and a precision of 98.1%.