Abstract
Heart disease is a serious medical problem that affects a large number of people and their lives; cardiac disease (CAD) is one of these threats. There is no substantial study in medical research that focuses on sophisticated planning techniques to uncover links and patterns in data. CAD is a serious health problem that affects people all over the world, especially in low- and middle-income nations. Therefore a low economic heart disease diagnostic application is necessary to crossover the subsequent limitation. Because of this social activity in the present situation, there should be an efficient as well as low economy heart disease diagnosis application design is compulsory. So, in this work RCNN based model has been designed for future generations. Various forms of Deep learning and intelligent technologies are used to extract important information in the case of predicting heart disease. However, the ideal findings’ discovering metrics such as sensitivity, F1-score, and reliability are not satisfactory. In this study of research, proposes a CAD risk prediction model using RCNN is a DL (Deep Learning) technique. In the next two decades, it will continue to be the leading cause of death. The major goal of this study is to apply the findings to existing methodologies. ML (Machine Learning) Techniques are employed to improve a doctor’s treatment decisions and diagnosis using Artificial Intelligence (AI). This work extremely examines the key components of systems, as well as relevant theories such as Gaussian Navies Bayes, Decision Tree (DT), K-NN, and RCNN. The suggested methodology combines AI and data mining to produce precise results with low error rates. This study sets the stage for the improvement of a novel risk prediction model in the field of CAD, with results such as accuracy 99.173%, precision 99.164%, recall 98.69%, sensitivity 98.3%, and specificity 0.0009. The findings that follow outperform the methods and compete with current technology.
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Saikumar, K., Rajesh, V. A machine intelligence technique for predicting cardiovascular disease (CVD) using Radiology Dataset. Int J Syst Assur Eng Manag 15, 135–151 (2024). https://doi.org/10.1007/s13198-022-01681-7
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DOI: https://doi.org/10.1007/s13198-022-01681-7