Comparative analysis of breast cancer and hypothyroid dataset using data mining classification techniques
Deepika Verma, Nidhi Mishra · 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI) · 2017
Data mining is a process of extracting useful information from large amount of data. Nowadays, data mining in healthcare is a popular field of high importance for providing prediction and a deeper understanding of medical data. Authors are using data mining techniques in the diagnosis of several diseases such as diabetes, strokes, cancer, kidney and heart disease etc. This paper discussed the classification of data mining techniques. In this paper, we use two classification algorithms NAÏVE BAYES AND MLP on the WEKA interface. The performances of these two algorithms have been analyzed on breast cancer and hypothyroid datasets. These two datasets have been chosen from UCI Machine Learning Repository. After analyzing the performances of both algorithm, found that naïve bayes gives the more accurate results.