Comparative analysis of Classification algorithms for hyperthyroidism

The most diagnosed disease prediction among worldwide, expression of biological data plays a vital role. It performs the critical task and a take apart to the hypothyroidism disease prediction. Interpreting the information from the biological data is an active area in bioinformatics research and it remains a complicated problem, due to the high dimensional and low sample size. This paper focuses on classifying the dataset to detect hypothyroidism. Hence, the classification is done by using cross-validation on four classification algorithms such as Naïve Bayes, SMO (Sequential Minimum Optimization algorithm), Ada Boost, and Random Forest. At last, the comparative analysis is carried out by using the performance measures. From the experiment result, it is inferred that the Random Forest algorithm provides better results out of the existing methods.