ARO–FTAMNet: An Artificial Rabbits Optimization Guided Feature- Gated Multi-Scale Network for Heart Disease Prediction with External Validation
Keywords:
Heart Disease Prediction; Artificial Rabbits’ Optimization; Feature Selection; Multi-Scale Neural Network; Adaptive Feature Gate; External Validation; Class Imbalance; Threshold Tuning.Abstract
Heart disease remains the leading cause of death worldwide, and most machine-learning studies claiming to predict it are evaluated only on the same small training dataset. Such results say little about how a model will perform on new patients. This paper presents ARO (Artificial Rabbits Optimization)– FTAMNet (Future Tuned Adaptive Meta Network), a prediction framework trained on a mixed (hybrid) heart-disease corpus and tested on a separate patient set the model never encounters during training. The framework has two parts. The first is ARO, which simultaneously searches for a small, useful set of input attributes and good network settings, using a single fitness score that rewards ranking quality and balanced agreement while penalizing large feature sets. The second is FTAMNet, a compact feed-forward network with four working blocks: a dense projection, an adaptive feature gate that learns how much of each internal signal to retain, a residual learning block, and a three-branch multi-scale stage whose outputs are merged before a sigmoid decision layer. The decision threshold is not fixed at 0.5; it is tuned on validation data only and then frozen. On the external test set of 1,025 records, the framework achieves an accuracy of 0.7727, a precision of 0.7901, a recall of 0.7586, an F1-score of 0.7740, an AUROC (Area Under the Receiver Operating Characteristic Curve) of 0.8582, an AUPRC (Area Under The Precision-Recall Curve) of 0.8617, and a Matthews correlation coefficient of 0.5460. Three deep baselines built on the same data [Deep Multi-Layer Perceptron (DeepMLP), Residual Multi-Layer Perceptron (ResMLP), and Attention-based Multi-Layer Perceptron (AttMLP)] fall far behind, with MCC (Matthews Correlation Coefficient) values ranging from −0.005 to 0.078, indicating that plain deep networks do not transfer across cohorts. Removing ARO from the pipeline raises precision but cuts recall by about 15 percentage points, so the optimizer mainly buys sensitivity, which is what a screening tool needs. The study shows that a small, well-tuned network with an honest external test is more useful than a large model scored on its own data.
Bangladesh Journal of Medical Science Vol. 25 No. 04 October’26 Page: 1099-1114
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Copyright (c) 2026 Rinaben Keshavlal Patel, Prakash Arumugam, Kaival Mehta, Mainul Haque
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