Background: selecting the correct statistical test and data mining method depends highly on
the measurement scale of data, type of variables, and purpose of the analysis. Different …

There is considerable interest in data analytics because of its value in informing decisions in
healthcare. Data variables can be derived from routinely collected records or from primary …

The concept of classification is used and examined by the scientific community for hundreds
of years. In this historical process, different methods and algorithms have been developed …

Clinical tests and epidemiological studies often produce large amounts of data, being
multivariate in nature. The respective analysis is, in most cases, of importance comparable …

As a new concept that emerged in the middle of 1990's, data mining can help researchers
gain both novel and deep insights and can facilitate unprecedented understanding of large …

The huge amount of healthcare data, coupled with the need for data analysis tools has
made data mining interesting research areas. Data mining tools and techniques help to …

Some data mining (DM) methods, or software tools, require normalized data, others rely on
categorized data, and some can accommodate multiple data scales. Each DM technique …

The characteristics of the patients have a great influence on the determination of the
probability of the stage of cancer. To determine the significant factors for assessing the …

Clustering is a method of grouping records in a database based on certain criteria. One
method of clustering is K-Means Clustering. K-Means Clustering divides data into multiple …

Computational Learning Approaches to Data Analytics in Biomedical Applications provides
a unified framework for biomedical data analysis using varied machine learning and …