Enhanced Cardiovascular Disease Prediction with a 1-D CNN Deep Learning Model - Dr.Deepak Dembla,; Sandeep Tomar,; Dr.Yogesh Chaba, (International Journal of Medical Toxicology and Legal Medicine, 2024)
This research work presents a new 1-D Convolutional Neural Network (CNN) model for predicting cardiac disease. The model utilizes the sophisticated features of deep learning to analyze complex medical data. The study emphasizes the urgent requirement for early identification of cardiovascular diseases and seeks to improve diagnosis accuracy with a novel predictive model. The research provides a thorough examination of the effectiveness of the proposed 1-D CNN by comparing it with conventional and modern machine learning techniques. This analysis sets new benchmarks in medical diagnostics. The proposed one-dimensional Convolutional Neural Network (1-D CNN) model exhibited exceptional performance, attaining a training accuracy of 97.79% and a test accuracy of 96.77%. It outperformed conventional algorithms such as Logistic Regression, Naïve Bayes, and Support Vector Machines (SVM), as well as other deep learning models like the Artificial Neural Network (ANN). Among all assessed models, it achieved the highest precision (94.73%), recall (100%), F1 score (97.29%), and AUC (96.15%). The results emphasize the accuracy and reliability of the model, especially in reducing the occurrence of false negatives, which is vital in medical diagnostics. The findings supports the incorporation of Convolutional Neural Networks (CNNs), particularly those specifically developed for analyzing one-dimensional medical signal data, into predictive diagnostic systems. The exceptional performance of 1-D CNN in predicting cardiac disease demonstrates the potential of deep learning technologies to transform the diagnosis of diseases by providing more sophisticated and accurate methods. The comparative research demonstrates the advancement of machine learning in healthcare, transitioning from conventional approaches to advanced models capable of managing complex medical data. This research makes significant contributions to the combining of machine learning and healthcare. It suggests a future where advanced algorithms can improve the early diagnosis of diseases, potentially leading to saved lives and better patient results. The suggested one-dimensional convolutional neural network (1-D CNN) model represents a notable breakthrough in the utilization of deep learning techniques to enhance the diagnosis of cardiac disease and the quality of patient treatment.