Assessing the Predictive Capability of a Machine Learning Model (Published)
The purpose of this study was to evaluate the effectiveness of an integrated machine learning system that has been put into place to help professionals predict how patients will respond to steroid treatment for glaucoma. The research employed a quantitative research methodology, utilizing descriptive statistics. Taro Yamane formula was applied in finding a suitable population size. Our study employed linear regression analysis to establish the correlation between the predictors, i.e the novel predicting system, and the dependent variable, which pertains to the effectiveness of forecasting a patient’s reaction to steroid treatment. The analysis showed that implementing a novel prediction technique would have a notable impact and efficiency in determining a persons status in pre-trabeculectomy evaluation. The p-value (0.000), which is less than the predefined significance level (Alpha) of 0.05—more specifically, 0.000<0.05—indicates the evidence for a significant finding. The calculated t-value (33.196) exceeds the critical t-value (1.960). Consequently, the correlation coefficient (R) of 0.920 demonstrates a highly robust positive effect.
Keywords: glaucoma treatment, machine learning, trabeculectomy