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All Journal Jurnal Manajemen Informatika Journal of Information Systems Engineering and Business Intelligence JIEET (Journal of Information Engineering and Educational Technology) Journal of Applied Data Sciences Abdimas: Papua Journal Of Community Service Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Journal of Informatics and Computer Science (JINACS) Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Andi Iwan Nurhidayat
Universitas Negeri Surabaya
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Nursing Public Health Social Sciences Transportation Other

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Hybrid Transformer-XGBOOST Model Optimized with Ant Colony Algorithm for Early Heart Disease Detection: A Risk Factor-Driven and Interpretable Method Pratama, Moch Deny; El Hakim, Faris Abdi; Aditia Syahputra, Dimas Novian; Dermawan, Dodik Arwin; Asmunin, Asmunin; Nudin, Salamun Rohman; Nurhidayat, Andi Iwan
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.969

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide, with significant socioeconomic consequences due to premature death and chronic disability. Although clinical screening techniques have evolved, early and accurate prediction of heart disease is still partial due to the limited capacity of conventional machine learning algorithms to model the complex nonlinear interactions among various contributing risk factors e.g., hypertension, diabetes, hyperlipidemia, and genetic predisposition. To address these challenges, this research introduces a hybrid framework that combines the Transformer architecture known for its robust self-attention mechanism and high representational capabilities with Ant Colony Optimization (ACO), a nature-inspired metaheuristic algorithm modeled on the foraging behavior of ants, to enable adaptive and efficient hyperparameter optimization. The proposed model processes structured clinical data by encoding categorical variables into embeddings and normalizing numerical features, resulting in a unified tabular representation suitable for transformer-based analysis. ACO improves model efficiency by optimizing key parameters e.g., embedding configuration, learning rate, and depth, reducing manual intervention and computational overhead. The proposed Hybrid Transformer-ACO model focuses on interpretable clinical features to provide actionable risk stratification. Model evaluation was performed using classification metrics e.g., accuracy, precision, recall, F1 score, and time complexity to measure predictive performance and computational efficiency during the training and inference phases. These evaluation criteria provide evidence of the model's diagnostic reliability, generalizability, and practical feasibility for clinical application.. The model achieved 100% accuracy, sensitivity, specificity, and F1-score, outperforming several models. Time complexity analysis demonstrated efficient training and testing, while the model interpretability supports transparency and trust.
Co-Authors Aditia Syahputra, Dimas Novian Agus Prihanto AMBRIANI, DINNI Anita Qoiriah ANITA QOIRIAH ARI KURNIAWAN Asmunin Asmunin Dodik Arwin Dermawan Dwi Fatrianto Suyatno Faris Abdi El Hakim, Faris Abdi Gali Firmansyah Hacimahmud Abdullayev, Vugar Iskandar Java, Muhammad Khalifah, Binti Mashuri, M Adamu Islam Nudin, Salamun Rohman Nudin Oliva , Diego Pratama, Moch Deny Rina Harimurti Rochmawati, Naim Rovita Van Joost, Sisilia SALAMUN ROHMAN NUDIN Sidhimantra, I Gde Agung Sri Soleimanian Gharehchopogh, Farhad Syahputra, Dimas Novian Aditia Widi Aribowo Wiyli Yustanti YUNI YAMASARI