Heart Disease Prediction Using a Novel Fuzzy-Enhanced CLSTM Model with Adaptive Stochastic Gradient Descent Optimization
Heart Disease Prediction Using a Novel Fuzzy-Enhanced CLSTM Model with Adaptive Stochastic Gradient Descent Optimization |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-3 |
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| Year of Publication : 2025 | ||
| Author : R. Parthiban, K. Santhosh Kumar |
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| DOI : 10.14445/22315381/IJETT-V73I3P135 | ||
How to Cite?
R. Parthiban, K. Santhosh Kumar, "Heart Disease Prediction Using a Novel Fuzzy-Enhanced CLSTM Model with Adaptive Stochastic Gradient Descent Optimization," International Journal of Engineering Trends and Technology, vol. 73, no. 3, pp. 503-516, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I3P135
Abstract
Cardiovascular Diseases (CVDs) are considered to be the predominant cause of the increase in death rates around the world. Hence, early detection is mandatory for managing and providing the affected persons with effective treatment. Normally, capturing complex patterns in medical data is much more difficult with the help of traditional machine learning methods. Although it is more effective, it is not able to handle the uncertainty and non-linearity problem that exists in the patient’s health metrics. In this study, a novel approach called Fuzzy-Enhanced CLSTM is proposed for heart disease prediction. This novel approach integrates Multilayer Fuzzy-based Convolutional Neural Networks (MFCNN) in correlation with the Bidirectional Long Short-Term Memory (BiLSTM) model. In this method, the fuzzy logic is leveraged to enhance the feature extraction process of CNN by making it more robust in dealing with imprecise and uncertain data. Including fuzzification will enhance the sensitivity to a certain extent by supporting the critical variations in the clinical parameters. Combining convolutional neural networks with the BiLSTM will capture the temporal dependencies in sequential data, enabling a more comprehensive understanding of patient history and trends over time. Hence, this model is suitable for both spatial feature extraction and temporal analysis. Fine-tuning of the model is performed using an Adaptive Stochastic Gradient Descent (ASGD) optimizer, which dynamically adjusts the learning rate during training. This helps faster convergence and prevents the model from getting stuck in local minima by improving overall prediction accuracy. The experimental results conducted by using publicly available datasets provide significant improvement in early and accurate heart disease detection and prediction by providing better accuracy and generalization compared to other traditional Methods.
Keywords
Heart disease prediction, Cardiovascular diseases, Fuzzy-based CNN, Adaptive stochastic gradient descent, BiLSTM, Deep learning.
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