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Machine Learning-Based Heart Disease Diagnosis: A Systematic Literature Review

arXiv 2112.06459 ↗First posted 13 Dec 2021cs.LGcited by 18

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Abstract (arXiv)

Heart disease is one of the significant challenges in today's world and one of the leading causes of many deaths worldwide. Recent advancement of machine learning (ML) application demonstrates that using electrocardiogram (ECG) and patient data, detecting heart disease during the early stage is feasible. However, both ECG and patient data are often imbalanced, which ultimately raises a challenge for the traditional ML to perform unbiasedly. Over the years, several data level and algorithm level solutions have been exposed by many researchers and practitioners. To provide a broader view of the existing literature, this study takes a systematic literature review (SLR) approach to uncover the challenges associated with imbalanced data in heart diseases predictions. Before that, we conducted a meta-analysis using 451 referenced literature acquired from the reputed journals between 2012 and November 15, 2021. For in-depth analysis, 49 referenced literature has been considered and studied, taking into account the following factors: heart disease type, algorithms, applications, and solutions. Our SLR study revealed that the current approaches encounter various open problems/issues when dealing with imbalanced data, eventually hindering their practical applicability and functionality.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 2 of its 69 references that are in the map. First posted on arXiv 13 Dec 2021.

Builds on

2 of 69 references.

References · 2

  1. 2017Fully Convolutional Architectures for Multi-Class Segmentation in Chest Radiographscited by 13
  2. 2019An Efficient Convolutional Neural Network for Coronary Heart Disease Predictioncited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 3; 2024: 4; 2025: 3; 2026: 2.

18 papers.

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  1. 2021Machine learning based disease diagnosis: A comprehensive reviewcited by 30
  2. 2024Review of multimodal machine learning approaches in healthcarecited by 30
  3. 2022Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16cited by 13
  4. 2023Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Frameworkcited by 9
  5. 2023Meta-learning in healthcare: A surveycited by 9
  6. 2022A k-additive Choquet integral-based approach to approximate the SHAP values for local interpretability in machine learningcited by 8
  7. 2023The Significance of Machine Learning in Clinical Disease Diagnosis: A Reviewcited by 4
  8. 2022Transfer learning and Local interpretable model agnostic based visual approach in Monkeypox Disease Detection and Classification: A Deep Learning insightscited by 3
  9. 2021Driver Drowsiness Detection Using Ensemble Convolutional Neural Networks on YawDDcited by 1
  10. 2024Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Reviewcited by 1
  11. 2024Stronger Baseline Models -- A Key Requirement for Aligning Machine Learning Research with Clinical Utilitycited by 1
  12. 2025Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Datacited by 1
6 more
  1. 2022Machine Learning-Based Disease Diagnosis:A Bibliometric Analysiscited by 0
  2. 2024Centralized and Federated Heart Disease Classification Models Using UCI Dataset and their Shapley-value Based Interpretabilitycited by 0
  3. 2025Harmonized Gradient Descent for Class Imbalanced Data Stream Online Learningcited by 0
  4. 2025Enhancing Interpretability of AR-SSVEP-Based Motor Intention Recognition via CNN-BiLSTM and SHAP Analysis on EEG Datacited by 0
  5. 2026Integrating Machine Learning Ensembles and Large Language Models for Heart Disease Prediction Using Voting Fusioncited by 0
  6. 2026Evaluating LLM-Generated Rules for Heart Disease Predictioncited by 0