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Disease prediction and diagnosis can be made with the help of machine learning models. Disease diagnosis applications have been developed and used extensively, especially with controlled machine learning techniques. This technique has enabled models to be created from historical data and these models have sometimes been used in diagnosis and treatment. Developing a system based on machine learning is not just about developing machine learning algorithms, but rather it is done by working on data step by step from start to finish in a way similar to the data mining process. For example, determining which variables are important and which are not important in the solution of a problem directly affects the quality of the solution. This process, called feature selection, determines which parameters will be used in the system to be installed. The feature selection process is often achieved by establishing the correct relationships between targeted data and predictive data.

The feature selection phase is followed by feature transformation. Data transformation, which is a method that improves data quality, has recently emerged as feature engineering, which includes feature studies performed to increase prediction success. Like feature selection, feature engineering will also affect the success of the result. Both feature selection and feature engineering also solve the problem of high dimensionality in data. Loss of data and methods of combating loss are also important. The fight against lost values ​​is sometimes carried out by estimating the lost value and sometimes by replacing it with other values.

It is also important to choose the learning models to be used when creating machine learning systems. There are four basic learning methods under the title of machine learning: supervised learning, unsupervised learning, semi-supervised learning, and reinforced learning. The latter two methods, semi-supervised learning is a type of learning that occurs with the combination of supervised and unsupervised techniques; reinforced learning is an agent-based learning technique where the decision is made according to the rewarding mechanism, and it is used to reveal the most possible solution to a subject that we did not have any previous knowledge about.

The most prominent techniques – supervised and unsupervised learning – will be explained in the following paragraphs.