Advanced Heart Disease Prediction Model Using Dung Beetle-Based Feature Selection and Bi-LSTM Classifier

In the modern environment, it is quite difficult to detect Heart Disease (HD) through early stage symptoms. Heart disease is the cause of mortality if the diagnosis is delayed including death occurs to prevent these issues. This paper proposes an innovative approach for the detection of heart disease from HD dataset, incorporating advanced techniques. Initially, data cleaning and data normalization is applied to the HD to eliminate or to find the duplicate values. Next, the images are segmented using K means clustering, allowing for the separation of relevant regions associated with HD. Features are then optimized from the segmented HD using the Dung Beetle Optimization (DBO) algorithm, capturing optimized values for further analysis. Finally, a Bidirectional Long Short Term Memory (BiLSTM) framework is proposed as a hybrid model to enhance the classification of HD images, leveraging both forward and backward temporal information to overcome challenges in disease analysis. The assessment of proposed work using python software reveals that the proposed framework with BiLSTM classifier ranks with improved accuracy of 91.56% when compared to the other techniques.