Evaluation of support vector machine and naive bayes classification algorithm in prognostic prediction and lung cancer diagnostic.

The primary objective of this study is to use Support Vector Machine (SVM) instead of the traditional Naive Bayes technique to extract patient-specific information from a dataset of lung cancer patients in order to calculate diagnostic accuracy (NB). The methods of Naive Bayes and Support Vector Machine are used in this study. Using the G power programme, we arrive at a sample size of 10 individuals per group with a pretest power of 80%, a significance level of 0.05, and a confidence interval (CI) of 95%. The innovative data analysis provided by Support Vector Machine for determining the likelihood of cancer is 92.45 percent, which is greater than the 84.67 percent provided by the Naive Bayes method. With a significance score of 0.88 (p>0.05), there is no statistically significant difference between the two groups. By extracting patient-specific information, the Support Vector Machine method outperforms the Naive bayes algorithm in making accurate cancer predictions. [ABSTRACT FROM AUTHOR]