# Applications of Machine Learning in Healthcare and Internet of Things (IOT): A Comprehensive Review

arXiv: https://arxiv.org/abs/2202.02868 · HTML: https://mlsanity.com/paper/2202.02868/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 108 references that are in the map.
- First posted on arXiv 6 Feb 2022.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [What is the State of Neural Network Pruning?](https://mlsanity.com/paper/2003.03033/) (2020)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [A Hybrid Approach to Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1812.03224/) (2018)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework](https://mlsanity.com/paper/2002.10671/) (2020)
- [The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review](https://mlsanity.com/paper/1511.05263/) (2015)
- [A Federated Learning Framework for Healthcare IoT devices](https://mlsanity.com/paper/2005.05083/) (2020)
- [Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges](https://mlsanity.com/paper/1907.08965/) (2019)
- [Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art](https://mlsanity.com/paper/2002.10610/) (2020)
- [DeepMSRF: A novel Deep Multimodal Speaker Recognition framework with Feature selection](https://mlsanity.com/paper/2007.06809/) (2020)
- [DRDr II: Detecting the Severity Level of Diabetic Retinopathy Using Mask RCNN and Transfer Learning](https://mlsanity.com/paper/2011.14733/) (2020)
- [Embodied AI-Driven Operation of Smart Cities: A Concise Review](https://mlsanity.com/paper/2108.09823/) (2021)
- [DRDr: Automatic Masking of Exudates and Microaneurysms Caused By Diabetic Retinopathy Using Mask R-CNN and Transfer Learning](https://mlsanity.com/paper/2007.02026/) (2020)
- [An Introduction to Advanced Machine Learning : Meta Learning Algorithms, Applications and Promises](https://mlsanity.com/paper/1908.09788/) (2019)
- [DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM](https://mlsanity.com/paper/2108.08095/) (2021)
- [Data Analytics for Smart cities: Challenges and Promises](https://mlsanity.com/paper/2109.05581/) (2021)

## Abstract (arXiv)

In recent years, smart healthcare IoT devices have become ubiquitous, but they work in isolated networks due to their policy. Having these devices connected in a network enables us to perform medical distributed data analysis. However, the presence of diverse IoT devices in terms of technology, structure, and network policy, makes it a challenging issue while applying traditional centralized learning algorithms on decentralized data collected from the IoT devices. In this study, we present an extensive review of the state-of-the-art machine learning applications particularly in healthcare, challenging issues in IoT, and corresponding promising solutions. Finally, we highlight some open-ended issues of IoT in healthcare that leaves further research studies and investigation for scientists.