Data Mining
Data mining includes all the stages of extracting meaningful data from a large dataset, formulation, sense-making, and purposeful use. The data used through the data mining stages are stored in large data warehouses and databases. The data used in this study were obtained from the databases of Selcuk University Medical School Hospital.

Preparation before Data Processing

A preliminary preparation work was conducted on the dataset first to be able to implement linear regression and J48 Decision Tree algorithms. Identity details such as name, surname and identity number were cleared off the dataset. At the data selection stage, the laboratory parameters intended to be used in COVID-19 diagnosis process were identified in the dataset with the help of doctors specialized in their fields.
Finally, the binary and nominal values in the dataset were converted into numeric data to be able to run the Linear Regression algorithm.
Absence of any missing values in the dataset used in the study provided advantage for the rest of the work.

WEKA (Waikato Environment for Knowledge Analysis)

Used in data mining, WEKA is an open-source software developed in Waikato University. This data mining instrument provides ready-made algorithms for data preparation and machine learning. It also provides tools for data and result visualization.
This study used the Nominal to Numeric and Binary to Numeric algorithms of WEKA at the dataset preparation stage. The Linear Regression and J48 Decision Tree algorithms were also run on WEKA.

Supervised Learning

In data mining, various algorithms are used to process large amounts of data. Designed to make estimations using data, these algorithms are divided into 2, Supervised and Unsupervised Learning. The Linear Regression and J48 algorithms used in this study are Supervised Learning algorithms. The main objective in Supervised Learning Algorithms is to infer, by training the dataset, a formula which has inputs and outputs and can be shown mathematically as y=f(x), In this formula x represents the input data, f the entire mathematical procedure applied to the data, and y estimated results from x.
Supervised Learning Algorithms are used in 2 areas, regression and classification. Both aim to form a model that can train itself using the dataset and make estimations.