There are many algorithms used in healthcare but the most common ones are:

Support Vector Machines

This is a standard machine learning algorithm that uses supervised learning methods for classification, regression, and detection of outliers. They are used for protein classification, image segmentation and text categorization.

Artificial Neural Networks

A group of deep learning algorithms inspired by the nervous system. More precisely, they are inspired by a neuron organization in animal brains. They consist of units – artificial neurons that receive a signal from the previous layer, process it and send it to the next layer. These networks can learn by “looking” at the examples and without direct programming by humans. Two types of neural networks that are commonly used in the field of medicine – Convolutional neural network and the Recurrent neural network.

Convolutional neural network

It is a feed-forward neural network, a deep learning algorithm that takes in an input image, assigns weights and biases to various features of the input image and after doing that becomes capable to distinguish images one from the other. With enough training its able to “learn” characteristics and filters which would have to be manually programmed in primitive AI methods.

Recurrent neural network

The basic difference between Convolutional and Recurrent neural network is that Convolutional neural network can ingest only a fixed size input thus generating fixed-size output whereas Recurrent neural network is more complex and can ingest arbitrary inputs thus generating arbitrary output data sizes although it does require much more input data than the Convolutional neural network.

Artificial neural networks are used in the fields of computer vision (image recognition, medical image analysis, and feature detection), natural language processing (semantic parsing, speech recognition, sentence modeling, search query retrieval, and text generation), and in early phases of drug discovery (filtering out potentially useful substances and prediction of their medical benefits).

Logistic Regression

It is a machine learning algorithm that uses regression to predict the state of a categorical dependent variable by using predictor variables. It is used for classifications and prediction of an event probability such as a disease risk assessment, a function that assists physicians in medical decisions.