Prediction of Brain Tumor Severity with Magnetic Resonance Imaging using Ridge Regression Over Recurrent Neural Network

The goal of the research work is to predict Brain tumors using the Ridge Regression algorithm and compare it with the Recurrent Neural Network. The research study involves two groups, Ridge Regression and Recurrent Neural Network, which were both examined using a sample size of 10 calculated with an alpha value of 0.05 and a confidence interval of 95% using the ClinCalc software. The image analysis dataset used in this study contains 82,347 tuples and was obtained from Kaggle.com. The Ridge Regression algorithm obtained an accuracy of 96.47%, while the Recurrent Neural Network classifier achieved an accuracy of 90.11%. An independent sample t-test conducted in SPSS statistical analysis indicated a statistically significant difference between the two algorithms with a p-value of 0.002 (p<0.05). The results suggest that the Ridge Regression algorithm significantly outperformed the Recurrent Neural Network algorithm in terms of accuracy in predicting Brain tumors.