A smart CardioSenseNet framework with advanced data processing models for precise heart disease detection

Heart diseases remain one of the leading causes of death worldwide. As a result, early and accurate diagnostics have become an urgent need for treatment and management. Most of the conventional methods adopted tend to have major drawbacks: the issues of accuracy, interpretability, and feature representation. This work, therefore, proposes CardioSenseNet, which may provide a new framework that can improve accuracy and efficiency in heart disease detection. Firstly, the approach introduces a few new methods: DGPN for data preprocessing, STHIO for feature selection, and SADNet for prediction. DGPN normalizes the data depending on the distribution characteristic, which improves the quality of the feature representation. STHIO adopts the Sheep Flock Optimization method for the exploration of features and Tuna Swarm Optimization for the exploitation of features, guaranteeing the optimality in feature selection. SADNet is one such deep learning model that tries to find the complicated pattern in high-dimensional data for better prediction accuracy. Extensive experiments on benchmark datasets such as Cleveland and CVD endorse the efficiency of CardioSenseNet with a high accuracy of 99 % and at an minimum loss of 0.12 %. The results thus indicate that CardioSenseNet is a promising solution for the detection of heart diseases with high accuracy and at an early stage; therefore, it will contribute significantly to cardiovascular healthcare developments. •This paper proposes CardioSenseNet, a novel framework for early and accurate heart disease detection.•It implements DGPN model for advanced data normalization, enhancing feature representation quality.•Also, it utilizes STHIO, a hybrid optimization technique combining Sheep Flock and Tuna Swarm methods for optimal feature selection.•Moreover, it achieves outstanding performance with high accuracy and a minimal loss of on Cleveland and CVD datasets.