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Review and Analysis of Machine Learning Techniques for Heart Attack Prediction
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 76 |
| Year of Publication: 2026 |
| Authors: Vidhi Patel, Nandini Chaudhari |
10.5120/ijca2026926307
|
Vidhi Patel, Nandini Chaudhari . Review and Analysis of Machine Learning Techniques for Heart Attack Prediction. International Journal of Computer Applications. 187, 76 ( Jan 2026), 30-38. DOI=10.5120/ijca2026926307
Abstract
Heart attacks are one of the major drivers of death, and predicting them early can save many lives. The current research ensured that only relevant and recent studies were included, mostly from 2021 to 2025, to maintain updated information. Today, machine learning is widely used to analyze patient data and find methods that can help doctors/clinicians to identify individuals who may be at high risk. However, after reviewing a number of research papers in this area, it is clear that recent studies still have several important limitations.One major problem is that many models do not use explainable AI (XAI), so doctors cannot clearly understand why a model predicts a patient as safe or at risk. Many studies still depend mainly on basic machine-learning methods or even regression techniques, which are not ideal for heart-attack prediction. More advanced methods such as deep learning, boosting, transfer learning, or hybrid models are rarely used. In cases where neural networks are applied, they often give unstable results because the datasets are small or imbalanced, which increases the chance of overfitting. Some studies also use undersampling or PCA, which can remove useful information when the data is already limited. Another problem is the lack of well-labeled medical data. Some researchers try to fix this using semi-supervised learning, but this requires more computation and is still difficult. Overall, the existing research shows a need for more advanced, explainable, and reliable machine-learning approaches that can handle small, noisy, and imbalanced medical datasets. This review highlights these gaps and aims to support the development of better and more trustworthy heart-attack prediction models.
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