Classification and Success Investigation of Biomedical Data Sets Using Supervised Machine Learning Models

Nowadays, information technologies are used in almost every field of Computer Science and Engineering. One of the most used areas is the health sector. With the use of digital hospital systems, patient data is now stored in a computerized environment, thereby creating biomedical data sets. These datasets, which are very large in size, are very difficult to analyze and interpret by a human. The machine learning algorithms are mainly used to analyze and interpreted these data sets. In this study, the performances of 5 machine learning algorithms have been compared by employing 5 different biomedical data sets and the results obtained were compared statistically. Results reveal that the KNN algorithm performs better for small biomedical data sets, whereas the ANN algorithm performs better for large data sets in terms of classification problem for the health sector.