Types of machine learning models used in healthcare : A research paper review.

this video is the first in a series of videos about the paper “Secure and Robust Machine Learning for Healthcare: A Survey”
This paper provides an overview of various applications of machine learning in healthcare and then presents challenges. and methods to ensure secure and privacy-preserving ML for healthcare applications. and Finally, the paper provides some insight into the current research challenges and promising directions for future research.
In this video, we will discuss different machine learning types and models in healthcare applications.
The first machine learning approach that is being used in healthcare is
1) Unsupervised Learning: and that is a machine learning technique that uses unlabeled data as training material for the machine learning model and the most used example for this approach is clustering, which is used to segment datasets to smaller groups based on similarities and common features in every piece of data. unsupervised learning can be used for anomaly detection, the model, in this case, tries to identify rare and suspicious items that don’t fit in the unlabeled dataset, And an example of unsupervised learning methods in healthcare is the prediction of heart diseases by clustering and classification.


And the second type of machine learning is

2) Supervised Learning, supervised learning models use labeled datasets as training materials to learn the association between inputs and outputs. In supervised learning, every data element consists of an input and output. In the case of object detection, the input is a picture of the object and the output is the name of this object, so we feed our unsupervised learning model a large number of examples to train on, so when it sees the same object in a new picture it can identify it, examples of supervised learning methods in health care include the classification of different types of lung nodules and recognition of different body organs from medical images



and the third type of ml is
3) Semi-supervised Learning, in semi-supervised learning models, the datasets used for training contains both labeled and unlabeled samples and its very useful to deal with datasets that contain a large amount of unlabeled data and fewer labeled data. So it can be used to acquire a sufficient amount of labeled data to be used for model training in supervised learning models, why is this so important? Because acquiring properly labeled data is very hard especially in the field of healthcare.


The fourth type of ml used in health care is
4) reinforcement learning. In which the system learns how to achieve a complex objective, to maximize the reward and that is the type of machine learning used in alpha go, alpha stare and open ai five to play go, starcraft and dota2.
Reinforcement learning was used in health care for applications as symptoms checking for disease diagnosis.

The next element of the paper is the applications of machine learning in healthcare, and that is the topic for the upcoming video,
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