Abstract
This paper introduces an advanced real-time system designed to predict cardiovascular diseases with integrated machine learning. Cardiovascular diseases, with the highest global mortality rate, have become increasingly prevalent, straining healthcare systems worldwide. These diseases, driven by factors such as high blood pressure, stress, age, gender, and cholesterol levels, have prompted numerous early diagnosis approaches, but their accuracy requires refinement due to the critical nature of cardiovascular diseases. This paper presents the DLCDD (Deep Learning based Cardiovascular Disease Diagnosis) framework, specifically addressing data-related challenges such as missing values and imbalances. The mean replacement technique is employed for handling missing values, while the Synthetic Minority Over-sampling Technique (SMOTE) is utilized to address imbalances in the dataset. In essence, DLCDD represents a significant advance in precise cardiovascular disease prediction, uniting deep learning with cutting-edge data processing and feature selection methods, addressing critical diagnostic challenges.
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