# The Significance of Machine Learning in Clinical Disease Diagnosis: A Review

arXiv: https://arxiv.org/abs/2310.16978 · HTML: https://mlsanity.com/paper/2310.16978/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 68 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction](https://mlsanity.com/paper/1703.00555/) (2017)
- [A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?](https://mlsanity.com/paper/2304.04780/) (2023)
- [Machine Learning-Based Heart Disease Diagnosis: A Systematic Literature Review](https://mlsanity.com/paper/2112.06459/) (2021)
- [Federated learning: Applications, challenges and future directions](https://mlsanity.com/paper/2205.09513/) (2022)
- [Recognizing Abnormal Heart Sounds Using Deep Learning](https://mlsanity.com/paper/1707.04642/) (2017)
- [RODEO: Robust DE-aliasing autoencOder for Real-time Medical Image Reconstruction](https://mlsanity.com/paper/1912.07519/) (2019)

## Built on by (most cited first, 4 of 4)

- [Progression and Challenges of IoT in Healthcare: A Short Review](https://mlsanity.com/paper/2311.12869/) (2023)
- [Medical Imaging Complexity and its Effects on GAN Performance](https://mlsanity.com/paper/2410.17959/) (2024)
- [A Scoping Review of AI-Driven Digital Interventions in Mental Health Care: Mapping Applications Across Screening, Support, Monitoring, Prevention, and Clinical Education](https://mlsanity.com/paper/2603.16204/) (2026)
- [Machine Learning and statistical classification of CRISPR-Cas12a diagnostic assays](https://mlsanity.com/paper/2501.04413/) (2025)

## Abstract (arXiv)

The global need for effective disease diagnosis remains substantial, given the complexities of various disease mechanisms and diverse patient symptoms. To tackle these challenges, researchers, physicians, and patients are turning to machine learning (ML), an artificial intelligence (AI) discipline, to develop solutions. By leveraging sophisticated ML and AI methods, healthcare stakeholders gain enhanced diagnostic and treatment capabilities. However, there is a scarcity of research focused on ML algorithms for enhancing the accuracy and computational efficiency. This research investigates the capacity of machine learning algorithms to improve the transmission of heart rate data in time series healthcare metrics, concentrating particularly on optimizing accuracy and efficiency. By exploring various ML algorithms used in healthcare applications, the review presents the latest trends and approaches in ML-based disease diagnosis (MLBDD). The factors under consideration include the algorithm utilized, the types of diseases targeted, the data types employed, the applications, and the evaluation metrics. This review aims to shed light on the prospects of ML in healthcare, particularly in disease diagnosis. By analyzing the current literature, the study provides insights into state-of-the-art methodologies and their performance metrics.