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Fractional Quokka swarm optimization enabled Hierarchical Convolutional Neural Network for IoT Driven heart disease
Kancherla Santoshi1, Subhani Shaik1, Ajit Kumar Rout2
1Department of Computer Science and Engineering, Gandhi Institute of Engineering and Technology University, Gunupur, Odisha 765022, India.
Insights
A new Fractional Quokka Swarm Optimization-enabled Hierarchical Convolutional Neural Network (FQSO_HCNN) model improves heart disease detection accuracy. This advanced method enhances early intervention and reduces mortality rates by analyzing complex patient data effectively.
Area of Science:
- Cardiology and Artificial Intelligence
- Medical Informatics and Machine Learning
Background:
- Heart disease is a leading global cause of mortality, necessitating advanced detection methods.
- Existing models struggle with heterogeneous data and complex physiological signal relationships.
- Early and accurate heart disease detection is crucial for reducing mortality and healthcare burdens.
Purpose of the Study:
- To introduce a novel Fractional Quokka Swarm Optimization-enabled Hierarchical Convolutional Neural Network (FQSO_HCNN) for enhanced heart disease detection.
- To develop an Internet of Medical Things (IoMT) system for secure collection and processing of patient data.
- To improve the accuracy and efficiency of automated heart disease diagnosis.
Main Methods:
- Data preprocessing using dual normalization and feature fusion with MobileNet.
- Addressing class imbalance with Adaptive Synthetic Sampling (ADASYN).
- Employing a Hierarchical Convolutional Neural Network (HCNN) optimized by Fractional Quokka Swarm Optimization (FQSO) for detection.
Main Results:
- The FQSO_HCNN model achieved high performance on the Heart Disease Database.
- Key metrics include Matthews Correlation Coefficient (MCC) of 0.946, accuracy of 96.52%, and True Positive Rate (TPR) of 95.78%.
- The model demonstrated superior capability in modeling complex, non-linear relationships in clinical data.
Conclusions:
- The proposed FQSO_HCNN model offers a significant advancement in automated heart disease detection.
- The IoMT-based system provides a secure and efficient platform for real-time health monitoring.
- This research highlights the potential of advanced AI techniques for improving cardiovascular disease management.
Abstract:
Heart disease remains one of the leading causes of mortality globally, resulting in millions of deaths annually and imposing a significant burden on healthcare infrastructure and clinical management systems. This alarming prevalence underscores the critical need for highly accurate, timely, and efficient detection methods to enable early intervention and reduce mortality rates. Moreover, classical detection models had limited adaptability to heterogeneous clinical data and an inadequate capability to model complex, non-linear relationships inherent in physiological signals and patient records. To solve such complexity, the heart disease detection is done using a novel model named Fractional Quokka swarm optimization-enabled Hierarchical Convolutional Neural Networks (FQSO_HCNN) in this research. The system model for Internet of Medical Things (IoMT) is established, and each patient is registered in IoT devices. Subsequently, the collected data is transmitted to a secure cloud server, where it is stored and utilized for performing heart disease detection. Initially, the heart disease data is preprocessed by dual normalization, which enhances data quality and consistency. Afterwards, feature fusion is done based on Jensen difference with MobileNet, which allows the integration of handcrafted and deep features to capture both global patterns and local variations in the data. This fusion improves the feature discriminability, providing a richer input representation for the next stage. To address class imbalance, adaptive synthetic sampling (ADASYN) is applied, which generates more informative minority samples. Finally, the Hierarchical Convolutional Neural Network (HCNN) is used for detecting heart disease, leveraging its layered architecture to model complex relationships in the data. Also, the hyperparameters are optimized using the proposed Fractional Quokka Swarm Optimization (FQSO), which combines Quokka Swarm Optimization (QSO) with Fractional Calculus (FC) to balance exploration and exploitation. The public benchmarking Heart Disease Database is used for evaluating the proposed model. Furthermore, FQSO_HCNN achieved Matthews Correlation Coefficient (MCC), False Omission Rate (FOR), True Positive Rate (TPR), and accuracy of 0.946, 0.051, 95.78 %, and 96.52 %, respectively.
