Integrated Deep Learning Model for Heart Disease Prediction Using Variant Medical Data Sets


  • Syed Anwar SRM Institute of Science and Technology
  • Senthil Kumar Thillaigovindan SRM Institute of science and Technology



CNN, Heart Disease, Multi Variant Data Set, DPW, Lung Images, Clinical Data Set, Disease Prediction


The Phenomenon of heart disease prediction has been well studied. There exist numerous techniques exist in literature which uses different features and methods. However, the accuracy of predicting heart disease is still a questioning factor. Towards improving the performance of heart disease prediction an efficient Integrated Deep Learning Model with Convolution Neural Network (IDLM_CNN) is presented in this article. The model considers various features from different data sets of lungs, diabetic and clinical features. The integrated model extracts texture features from lung images in form of mass values. Similarly, the blood glucose, BMI and other diabetic features are extracted from diabetic data set. Also, lifestyle features like physical habits, food habits and smoking habits are extracted from clinical data sets. Such features extracted from various data sets are combined and trained with Convolution neural network to support the disease prediction. The method convolves the features of lungs and combines with other features to compute Disease Prone Weight (DPW) towards cardiac disease. Based on the value of DPW, the method predicts the possibility of heart disease.  The proposed method increases the performance of disease prediction and reduces the false ratio.

Author Biography

Syed Anwar, SRM Institute of Science and Technology

Syed Anwar hussainy Fazlur is a Research Scholar from the Department of Computing Technologies, SRM Institute of Science and Technology, Chennai, India. Doing His Doing his research in the area of Artificial Intelligence.




How to Cite

Fazlur, S. A. H. . ., & Thillaigovindan, S. K. (2022). Integrated Deep Learning Model for Heart Disease Prediction Using Variant Medical Data Sets. International Journal of Online and Biomedical Engineering (iJOE), 18(09), pp. 178–191.