Why the study?
Coronary Heart Disease remains a significant global health concern, motivating the use of readily available clinical data and advanced machine learning techniques to enhance early risk prediction.
Does a Multi-Layer Perceptron (MLP) neural network accurately predict Coronary Heart Disease (CHD) risk using demographic and measurement features from the Sleep Heart Health Study (SHHS) dataset?
Does a Multi-Layer Perceptron (MLP) neural network accurately predict Coronary Heart Disease (CHD) risk using demographic and measurement features from the Sleep Heart Health Study (SHHS) dataset?
A Multi-Layer Perceptron neural network using readily available demographic and measurement data can predict coronary heart disease risk with over 70% accuracy, potentially enhancing early risk prediction and personalized medicine.
Coronary Heart Disease (CHD) remains a significant global health concern. This study utilizes the Sleep Heart Health Study (SHHS) dataset, encompassing demographic features, measurement features, and combination of these two categories, to develop a predictive model for CHD risk. The Multi-Layer Perceptron (MLP) neural network is employed to capture intricate relationships within the data. An accuracy mostly greater than 70% in CHD risk prediction is exhibited by the MLP model when categories of selected features are fed to it. This research emphasizes the importance of using readily available data for physicians and advanced machine learning technique to enhance early CHD risk prediction. These findings have the potential to improve personalized medicine and targeted interventions for CHD prevention and management.
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Shirazi et al. (2023) studied Coronary Heart Disease. Multi-Layer Perceptron (MLP) neural network was evaluated on CHD risk prediction accuracy.
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