# Machine learning based disease diagnosis: A comprehensive review

arXiv: https://arxiv.org/abs/2112.15538 · HTML: https://mlsanity.com/paper/2112.15538/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 144 references that are in the map.
- First posted on arXiv 31 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks](https://mlsanity.com/paper/2003.11617/) (2020)
- [Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2003.10849/) (2020)
- [COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images](https://mlsanity.com/paper/2003.11055/) (2020)
- [Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection](https://mlsanity.com/paper/2003.10769/) (2020)
- [Machine Learning-Based Heart Disease Diagnosis: A Systematic Literature Review](https://mlsanity.com/paper/2112.06459/) (2021)
- [Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models](https://mlsanity.com/paper/1801.00880/) (2018)
- [Recognizing Abnormal Heart Sounds Using Deep Learning](https://mlsanity.com/paper/1707.04642/) (2017)
- [End-to-End Parkinson Disease Diagnosis using Brain MR-Images by 3D-CNN](https://mlsanity.com/paper/1806.05233/) (2018)
- [Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network](https://mlsanity.com/paper/1707.08386/) (2017)

## Built on by (most cited first, 30 of 30)

- [An Information Theoretic Perspective on Conformal Prediction](https://mlsanity.com/paper/2405.02140/) (2024)
- [Continual Learning in Medical Imaging: A Survey and Practical Analysis](https://mlsanity.com/paper/2405.13482/) (2024)
- [PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction](https://mlsanity.com/paper/2312.04658/) (2023)
- [Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks](https://mlsanity.com/paper/2405.01196/) (2024)
- [Dermacen Analytica: A Novel Methodology Integrating Multi-Modal Large Language Models with Machine Learning in tele-dermatology](https://mlsanity.com/paper/2403.14243/) (2024)
- [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)
- [Balancing Two Classifiers via A Simplex ETF Structure for Model Calibration](https://mlsanity.com/paper/2504.10007/) (2025)
- [OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation](https://mlsanity.com/paper/2508.17524/) (2025)
- [Imbalanced Class Data Performance Evaluation and Improvement using Novel Generative Adversarial Network-based Approach: SSG and GBO](https://mlsanity.com/paper/2210.12870/) (2022)
- [Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review](https://mlsanity.com/paper/2405.13832/) (2024)
- [Mechanism Learning: reverse causal inference in the presence of multiple unknown confounding through causally weighted Gaussian mixture models](https://mlsanity.com/paper/2410.20057/) (2024)
- [ADAPT: A Game-Theoretic and Neuro-Symbolic Framework for Automated Distributed Adaptive Penetration Testing](https://mlsanity.com/paper/2411.00217/) (2024)
- [From EMR Data to Clinical Insight: An LLM-Driven Framework for Automated Pre-Consultation Questionnaire Generation](https://mlsanity.com/paper/2508.00581/) (2025)
- [Let the Experts Speak: Improving Survival Prediction & Calibration via Mixture-of-Experts Heads](https://mlsanity.com/paper/2511.09567/) (2025)
- [Machine Learning-Based Disease Diagnosis:A Bibliometric Analysis](https://mlsanity.com/paper/2201.02755/) (2022)
- [Invariant Scattering Transform for Medical Imaging](https://mlsanity.com/paper/2304.10582/) (2023)
- [Contrastive Learning as Kernel Approximation](https://mlsanity.com/paper/2309.02651/) (2023)
- [Ensuring Ground Truth Accuracy in Healthcare with the EVINCE framework](https://mlsanity.com/paper/2405.15808/) (2024)
- [Advancing Melanoma Diagnosis with Self-Supervised Neural Networks: Evaluating the Effectiveness of Different Techniques](https://mlsanity.com/paper/2407.14628/) (2024)
- [Prediction and Detection of Terminal Diseases Using Internet of Medical Things: A Review](https://mlsanity.com/paper/2410.00034/) (2024)
- [On the (In)Significance of Feature Selection in High-Dimensional Datasets](https://mlsanity.com/paper/2508.03593/) (2025)
- [A Systematic Review on the Generative AI Applications in Human Medical Genomics](https://mlsanity.com/paper/2508.20275/) (2025)
- [Stacked Ensemble of Fine-Tuned CNNs for Knee Osteoarthritis Severity Grading](https://mlsanity.com/paper/2511.22143/) (2025)
- [A Context-Aware Temporal Modeling through Unified Multi-Scale Temporal Encoding and Hierarchical Sequence Learning for Single-Channel EEG Sleep Staging](https://mlsanity.com/paper/2512.22976/) (2025)
- [Multi-Task Anti-Causal Learning for Reconstructing Urban Events from Residents' Reports](https://mlsanity.com/paper/2603.11546/) (2026)
- [Survival Meets Classification: A Novel Framework for Early Risk Prediction Models of Chronic Diseases](https://mlsanity.com/paper/2603.11598/) (2026)
- [A Systematic Evaluation of Imbalance Handling Methods in Biomedical Binary Classification](https://mlsanity.com/paper/2605.14147/) (2026)
- [Transcript-Free Lightweight Detection of Alzheimer's Disease from Spontaneous Speech Using Handcrafted MFCC-Dominant Acoustic Biomarkers](https://mlsanity.com/paper/2607.10168/) (2026)
- [Interpretable and Calibrated Classification of Clinical Data Using Supervised Feature Binarization](https://mlsanity.com/paper/2607.15394/) (2026)
- [What Is Missing in Surgical Risk Stratification and Outcome Prediction: A Scoping Review of End-to-End Machine Learning Approaches](https://mlsanity.com/paper/2607.29090/) (2026)

## Abstract (arXiv)

Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges to developing the early diagnosis tool and effective treatment. Machine Learning (ML), an area of Artificial Intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how Machine Learning (ML) and Deep Learning (DL) are being used to help in the early identification of numerous diseases. To begin, a bibliometric study of the publication is given using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in Machine Learning-based Disease Diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, the paper highlights key results and provides insight into future trends and opportunities in the MLBDD area.