Optimization of Coronary Heart Disease Risk Prediction Using Extreme Learning Machine Algorithm (Case Study: Patients of Dr. Soeselo Hospital) - Iswanti, Arie; Isnanto, R. Rizal; Widodo, Catur Edi (Scientific Journal of Informatics, 2025)
Purpose: Coronary heart disease (CHD) is the leading cause of death globally, with 17.8 million deaths reported by the WHO in 2021. Early detection remains a major challenge due to low public awareness and dependence on manual diagnostic procedures. These limitations necessitate the development of automated and accurate predictive models. This study aims to construct a CHD risk prediction model using the Extreme Learning Machine (ELM) algorithm. The research addresses a critical limitation in existing models, namely, poor performance on minority classes (CHD stages 2–4), caused by data imbalance. To overcome this, oversampling techniques such as Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) are applied. The objective is to improve classification performance, particularly in high-risk categories, and to enhance the model’s generalisation capability for real-world implementation. Methods: This research implements the Extreme Learning Machine (ELM) algorithm to achieve optimal prediction results. The data used in this study as the initial database of patients consists of gender, age, height, weight, whether they have diabetes or not, the number of cigarettes consumed daily, and blood pressure. The data will be the main component in building the heart disease prediction system. The prediction classes are: no heart disease, stage 1 heart disease, stage 2 heart disease, stage 3 heart disease, and stage 4 heart disease. The total number of dataset are 521 data points, with 70% of the training data amounting to 364 patients, and 30% of the test data amounting to 157 patients. The data collection process uses patient data from RSUD Dr. Soeselo, Tegal Regency, Central Java, for the years 2023 and 2024. Result: The research successfully developed and evaluated an Extreme Learning Machine (ELM) algorithm for Coronary Heart Disease (CHD) risk prediction using patient data from Dr. Soeselo Hospital. The model achieved an overall accuracy of 82% on the dataset of 157 patients, demonstrating a promising capability for automated risk assessment. Novelty: This predictive model can be utilised in the medical field to facilitate the early detection of heart disease or other risks. This model will soon be introduced in hospitals in the Tegal Regency and City area, Central Java.