mlsanityData to 14 Oct 2026

Machine learning based disease diagnosis: A comprehensive review

arXiv 2112.15538 ↗First posted 31 Dec 2021cs.LGcited by 30

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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.

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

10 of 144 references.

References · 10

  1. 2020COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Imagescited by 264
  2. 2020Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networkscited by 92
  3. 2020Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networkscited by 86
  4. 2020COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Imagescited by 60
  5. 2020Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detectioncited by 46
  6. 2021Machine Learning-Based Heart Disease Diagnosis: A Systematic Literature Reviewcited by 18
  7. 2018Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse modelscited by 12
  8. 2017Recognizing Abnormal Heart Sounds Using Deep Learningcited by 9
  9. 2018End-to-End Parkinson Disease Diagnosis using Brain MR-Images by 3D-CNNcited by 5
  10. 2017Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Networkcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 10; 2025: 8; 2026: 6.

30 papers.

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  1. 2024An Information Theoretic Perspective on Conformal Predictioncited by 21
  2. 2024Continual Learning in Medical Imaging: A Survey and Practical Analysiscited by 9
  3. 2023PAC-Bayes Generalization Certificates for Learned Inductive Conformal Predictioncited by 6
  4. 2024Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networkscited by 5
  5. 2024Dermacen Analytica: A Novel Methodology Integrating Multi-Modal Large Language Models with Machine Learning in tele-dermatologycited by 4
  6. 2022Transfer learning and Local interpretable model agnostic based visual approach in Monkeypox Disease Detection and Classification: A Deep Learning insightscited by 3
  7. 2025Balancing Two Classifiers via A Simplex ETF Structure for Model Calibrationcited by 3
  8. 2025OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretationcited by 3
  9. 2022Imbalanced Class Data Performance Evaluation and Improvement using Novel Generative Adversarial Network-based Approach: SSG and GBOcited by 1
  10. 2024Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Reviewcited by 1
  11. 2024Mechanism Learning: reverse causal inference in the presence of multiple unknown confounding through causally weighted Gaussian mixture modelscited by 1
  12. 2024ADAPT: A Game-Theoretic and Neuro-Symbolic Framework for Automated Distributed Adaptive Penetration Testingcited by 1
18 more
  1. 2025From EMR Data to Clinical Insight: An LLM-Driven Framework for Automated Pre-Consultation Questionnaire Generationcited by 1
  2. 2025Let the Experts Speak: Improving Survival Prediction & Calibration via Mixture-of-Experts Headscited by 1
  3. 2022Machine Learning-Based Disease Diagnosis:A Bibliometric Analysiscited by 0
  4. 2023Invariant Scattering Transform for Medical Imagingcited by 0
  5. 2023Contrastive Learning as Kernel Approximationcited by 0
  6. 2024Ensuring Ground Truth Accuracy in Healthcare with the EVINCE frameworkcited by 0
  7. 2024Advancing Melanoma Diagnosis with Self-Supervised Neural Networks: Evaluating the Effectiveness of Different Techniquescited by 0
  8. 2024Prediction and Detection of Terminal Diseases Using Internet of Medical Things: A Reviewcited by 0
  9. 2025On the (In)Significance of Feature Selection in High-Dimensional Datasetscited by 0
  10. 2025A Systematic Review on the Generative AI Applications in Human Medical Genomicscited by 0
  11. 2025Stacked Ensemble of Fine-Tuned CNNs for Knee Osteoarthritis Severity Gradingcited by 0
  12. 2025A Context-Aware Temporal Modeling through Unified Multi-Scale Temporal Encoding and Hierarchical Sequence Learning for Single-Channel EEG Sleep Stagingcited by 0
  13. 2026Multi-Task Anti-Causal Learning for Reconstructing Urban Events from Residents' Reportscited by 0
  14. 2026Survival Meets Classification: A Novel Framework for Early Risk Prediction Models of Chronic Diseasescited by 0
  15. 2026A Systematic Evaluation of Imbalance Handling Methods in Biomedical Binary Classificationcited by 0
  16. 2026Transcript-Free Lightweight Detection of Alzheimer's Disease from Spontaneous Speech Using Handcrafted MFCC-Dominant Acoustic Biomarkerscited by 0
  17. 2026Interpretable and Calibrated Classification of Clinical Data Using Supervised Feature Binarizationcited by 0
  18. 2026What Is Missing in Surgical Risk Stratification and Outcome Prediction: A Scoping Review of End-to-End Machine Learning Approachescited by 0