Prediction of Heart Attacks Based on Data Analysis with Deep Leaning Approach

Abstract

Nowadays, cardiovascular disease continues to be  prominent cause of mortality on a global scale, underscoring the necessity for reliable predictive frameworks to facilitate timely detection and intervention. Heart disease is one of the leading causes of death among men and women of different races and ethnicities [1]. Within this investigation, a new methodology is proposed for forecasting cardiac incidents through the utilization of sophisticated deep learning methodologies applied to extensive health-related datasets. The accuracy of  model outcomes is contingent upon the cleanliness of the data and the refinement of our machine through the utilization of said data. Thereby enhancing the precision of the model outcomes. Our methodology encompasses the preprocessing of varied patient data, encompassing medical backgrounds and clinical measurements, alongside the application of deep learning Classification methods such as Naïve Bayes and Decision Trees. Subsequently, a deep learning model is formulated and trained on this data to discern intricate patterns indicative of imminent cardiac occurrences. Deep learning surpasses traditional machine learning classifiers in performance when confronted with extensive datasets. [9]. The findings underscore the effectiveness of employing the deep learning approach in forecasting cardiac events accurately, demonstrating promising parameters like sensitivity, specificity, and the area under the receiver operating characteristic curve. Additionally, we analyze the possible clinical implications of our findings and suggest avenues for future research focused on enhancing the accuracy and relevance of predictive algorithms for assessing cardiovascular risk.