Key result
Novel RO-RNN-LSTM model detects atherosclerosis with ~99% accuracy across cardiovascular datasets.
Why the study?
Early detection and intervention are needed to minimize the adverse health effects of atherosclerosis, motivating the development of an artificial intelligence framework for accurate disease detection and classification.
Does a hybrid REMORA Optimized Recurrent Neural Network with Long Short-Term Memory (RO-RNN-LSTM) algorithm improve diagnostic classification accuracy for atherosclerosis compared to standard machine learning models in publicly available cardiovascular datasets?
Does a hybrid REMORA Optimized Recurrent Neural Network with Long Short-Term Memory (RO-RNN-LSTM) algorithm improve diagnostic classification accuracy for atherosclerosis compared to standard machine learning models in publicly available cardiovascular datasets?
This computational study demonstrates that integrating Remora Optimization feature selection with a hybrid RNN-LSTM deep learning architecture achieves up to 98.5% accuracy in classifying atherosclerosis and cardiovascular disease from public tabular datasets.
Atherosclerosis is a medical condition where plaque accumulates in the arteries. It is important to detect and intervene early to minimize the negative effects of atherosclerosis on a person's health. The World Health Organization (WHO) states that 17.9 million deaths globally are caused by cardiovascular diseases (CVDs), the primary cause of death in the country. Several medical innovations and advancements were being explored in the field of atherosclerosis. The proposed research aims to build a system based on artificial intelligence (AI) automatic framework for an accurate detection and classification of Atherosclerosis using a hybrid REMORA Optimized Recurrent Neural Network with Long Short. Term Memory (RO-RNN-LSTM) method. In this work, various techniques for feature selection, preprocessing, and classification are implemented to achieve more accuracy. The min-max normalization followed by, the relevant features from the preprocessed data are extracted using the innovative REMORA Optimization method. In order to maximize accuracy and minimize classifier time, the dimensionality of features was reduced in this work. The publicly available datasets such as Cleveland, Kaggle (CVD) and Stulong are used to evaluate the proposed algorithms. The prediction findings indicate that utilizing the RO-RNN-LSTM algorithms improves accuracy up to 98.5%, sensitivity to 98.7%, specificity to 95.4%, recall to 98.7% and f1-score to 98.7%. The RO-RNN-LSTM exhibits superior performance outcomes while processing large dimensional medical datasets, according to the overall comparison investigation and analysis.
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Kumar et al. (2026) studied Atherosclerosis and cardiovascular disease (n=71,722). Remora Optimized Recurrent Neural Network with Long Short-Term Memory (RO-RNN-LSTM) classification model vs. Conventional machine learning and deep learning models (Random Forest, SVM, Decision Tree, Adaboost, RIFSACM) was evaluated on Classification accuracy for atherosclerosis detection. The RO-RNN-LSTM algorithm achieved up to 98.5% accuracy, 98.7% sensitivity, and 95.4% specificity for detecting atherosclerosis across public cardiovascular datasets.
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