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

arXiv: https://arxiv.org/abs/2112.06459 · HTML: https://mlsanity.com/paper/2112.06459/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 69 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [Fully Convolutional Architectures for Multi-Class Segmentation in Chest Radiographs](https://mlsanity.com/paper/1701.08816/) (2017)
- [An Efficient Convolutional Neural Network for Coronary Heart Disease Prediction](https://mlsanity.com/paper/1909.00489/) (2019)

## Built on by (most cited first, 18 of 18)

- [Review of multimodal machine learning approaches in healthcare](https://mlsanity.com/paper/2402.02460/) (2024)
- [Machine learning based disease diagnosis: A comprehensive review](https://mlsanity.com/paper/2112.15538/) (2021)
- [Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16](https://mlsanity.com/paper/2206.01862/) (2022)
- [Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework](https://mlsanity.com/paper/2305.04401/) (2023)
- [Meta-learning in healthcare: A survey](https://mlsanity.com/paper/2308.02877/) (2023)
- [A k-additive Choquet integral-based approach to approximate the SHAP values for local interpretability in machine learning](https://mlsanity.com/paper/2211.02166/) (2022)
- [The Significance of Machine Learning in Clinical Disease Diagnosis: A Review](https://mlsanity.com/paper/2310.16978/) (2023)
- [Transfer learning and Local interpretable model agnostic based visual approach in Monkeypox Disease Detection and Classification: A Deep Learning insights](https://mlsanity.com/paper/2211.05633/) (2022)
- [Driver Drowsiness Detection Using Ensemble Convolutional Neural Networks on YawDD](https://mlsanity.com/paper/2112.10298/) (2021)
- [Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review](https://mlsanity.com/paper/2405.13832/) (2024)
- [Stronger Baseline Models -- A Key Requirement for Aligning Machine Learning Research with Clinical Utility](https://mlsanity.com/paper/2409.12116/) (2024)
- [Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Data](https://mlsanity.com/paper/2501.15973/) (2025)
- [Machine Learning-Based Disease Diagnosis:A Bibliometric Analysis](https://mlsanity.com/paper/2201.02755/) (2022)
- [Centralized and Federated Heart Disease Classification Models Using UCI Dataset and their Shapley-value Based Interpretability](https://mlsanity.com/paper/2408.06183/) (2024)
- [Harmonized Gradient Descent for Class Imbalanced Data Stream Online Learning](https://mlsanity.com/paper/2508.11353/) (2025)
- [Enhancing Interpretability of AR-SSVEP-Based Motor Intention Recognition via CNN-BiLSTM and SHAP Analysis on EEG Data](https://mlsanity.com/paper/2512.06730/) (2025)
- [Integrating Machine Learning Ensembles and Large Language Models for Heart Disease Prediction Using Voting Fusion](https://mlsanity.com/paper/2602.22280/) (2026)
- [Evaluating LLM-Generated Rules for Heart Disease Prediction](https://mlsanity.com/paper/2609.13192/) (2026)

## 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.