# Recent advancement in Disease Diagnostic using machine learning: Systematic survey of decades, comparisons, and challenges

arXiv: https://arxiv.org/abs/2308.01319 · HTML: https://mlsanity.com/paper/2308.01319/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 85 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [Deep learning cardiac motion analysis for human survival prediction](https://mlsanity.com/paper/1810.03382/) (2018)
- [Modeling and forecasting Spread of COVID-19 epidemic in Iran until Sep 22, 2021, based on deep learning](https://mlsanity.com/paper/2103.08178/) (2021)
- [Deep Neural Network Based Ensemble learning Algorithms for the healthcare system (diagnosis of chronic diseases)](https://mlsanity.com/paper/2103.08182/) (2021)
- [Predicting drug response of tumors from integrated genomic profiles by deep neural networks](https://mlsanity.com/paper/1805.07702/) (2018)
- [Contactless Cardiac Arrest Detection Using Smart Devices](https://mlsanity.com/paper/1902.00062/) (2019)
- [Hybrid stacked ensemble combined with genetic algorithms for Prediction of Diabetes](https://mlsanity.com/paper/2103.08186/) (2021)

## Built on by (most cited first, 1 of 1)

- [Review of deep learning in healthcare](https://mlsanity.com/paper/2310.00727/) (2023)

## Abstract (arXiv)

Computer-aided diagnosis (CAD), a vibrant medical imaging research field, is expanding quickly. Because errors in medical diagnostic systems might lead to seriously misleading medical treatments, major efforts have been made in recent years to improve computer-aided diagnostics applications. The use of machine learning in computer-aided diagnosis is crucial. A simple equation may result in a false indication of items like organs. Therefore, learning from examples is a vital component of pattern recognition. Pattern recognition and machine learning in the biomedical area promise to increase the precision of disease detection and diagnosis. They also support the decision-making process's objectivity. Machine learning provides a practical method for creating elegant and autonomous algorithms to analyze high-dimensional and multimodal bio-medical data. This review article examines machine-learning algorithms for detecting diseases, including hepatitis, diabetes, liver disease, dengue fever, and heart disease. It draws attention to the collection of machine learning techniques and algorithms employed in studying conditions and the ensuing decision-making process.