Recently, research has been actively conducted to determine which drugs are the most effective treatments without side effects for patients with Parkinson's disease, heart disease, and chronic diseases, using artificial intelligence generation technology and virtual model-based technology. In this paper, we performed a computer simulation to self-diagnose patients at risk of heart disease and predict risk levels on a web-based platform. Furthermore, this paper established a hypothesis theory on the probability of heart disease risk occurrence, and performed factor analysis, correlation analysis, and fuzzy inference computer simulations to predict heart disease risk probability and improve performance. The results of the computer simulations confirmed that the fuzzy inference method improved heart disease risk prediction by over 10% compared to existing methods.
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Abstract 1. INTRODUCTION 2. Self-diagnosis of stroke 3. WEB-based judgment of heart disease 4. Computer simulation and results 5. Conclusion References