HEART DISEASE DETECTION USING DEEP LEARNING IN HEALTHCARE - Mandhare, Suchita Prakash; Alaskar, Kamal Miyalal (ShodhKosh: Journal of Visual and Performing Arts, 2024)
In recent years, healthcare prediction has played an important role in saving lives. Artificial intelligence for understanding crucial data relations and transforming them into valuable data for prediction is increasingly used in the healthcare sector. The healthcare sector is growing fast due to Artificial Intelligence. Deep ML models playing a significant role in disease detection and pattern identification. These models even can help to diagnose that disease which cannot be detected easily by human beings. By applying deep learning models to Electronic Health Records, an experimental comparative research design is implemented using DNN, MLP, CNN, LightGBM, TabNet, and CatBoost models. The performance of each model is evaluated and compared using different metrics: Accuracy, F1 Score, and ROC AUC. The ensemble model is found significant in terms of Accuracy and F1 Score, whereas LightGBM and CatBoost are found effective in terms of ROC AUC. The study concludes that to detect heart disease using patient data on key health indicators, Machine Learning and Deep Learning methods—especially the ensemble model—are significant. The main goal of this research is to decide which patient has the maximum probability of heart disease based on several medical values. To predict the probability of heart disease, the author created an ensemble model using the patient’s medical records.