Advancing Heart Disease Forecasting: Integrating CNN-LSTM Models with Feature Enhancement for Improved Predictive Accuracy

Nidhi Mishra; Ghorpade Bipin Shivaji

doi:10.25163/angiotherapy.899871

Angiogenesis, Inflammation & Therapeutics | Online ISSN 2207-872X

RESEARCH ARTICLE (Open Access)

Previous Next Contents Vol 8 (9)

Advancing Heart Disease Forecasting: Integrating CNN-LSTM Models with Feature Enhancement for Improved Predictive Accuracy

Nidhi Mishra ¹*, Ghorpade Bipin Shivaji ¹

+ Author Affiliations

Journal of Angiotherapy 8 (9) 1-6 https://doi.org/10.25163/angiotherapy.899871

Submitted: 12 July 2024 Revised: 27 August 2024 Published: 03 September 2024

Abstract

Background: Heart failure (HF) is the leading cause of death worldwide, with accurate early diagnosis being challenging due to the disease's subtle symptoms and the requirement for extensive medical expertise. The development of predictive models is crucial for early intervention. Objective: This study proposes a method for Dynamic Forecasting (DF) of Heart Diseases (HD) using a Deep Learning (DL) model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The aim is to create an automatic, accurate, and cost-effective system for predicting cardiac valve failure. Methods: The study introduces a Hybrid DL model combining CNN and LSTM with Feature Enhancement (FE) and Comprehensible Artificial Intelligence (CAI) techniques. The CNN extracts features from clinical data, which are then processed by the LSTM model to handle temporal dependencies. The model's effectiveness was evaluated using a freely available cardiovascular disease dataset. Accuracy was assessed with and without FE. Results: The CNN-LSTM model achieved an accuracy of 83.5% with FE and 88.2% without FE. The model demonstrated superior performance in terms of sensitivity (80.04% with FE), specificity (87.11% with FE), and overall accuracy compared to other methods including Support Vector Machines (SVM), Decision Trees (DT), and Random Forests (RF). Conclusion: The proposed CNN-LSTM model offers a significant improvement in forecasting heart disease dynamics by effectively integrating CNN for feature extraction and LSTM for temporal analysis. This hybrid approach, combined with advanced feature enhancement and explainable AI techniques, provides a more accurate and comprehensible prediction tool for early heart disease detection.

Keywords: Heart Diseases, Dynamic Forecasting, Deep Learning, CNN, LSTM, Artificial Intelligence

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