Breast Cancer Disease Prediction Using Random Forest Regression and Gradient Boosting Regression

Abstract

The current research endeavors to evaluate the efficacy of regression-based machine learning algorithms through an assessment of their performance using diverse metrics. The focus of our study involves the implementation of the breast cancer Wisconsin (Diagnostic) dataset, employing both the random forest and gradient-boosting regression algorithms. In our comprehensive performance analysis, we utilized key metrics such as Mean Squared Error (MSE), R-squared, Mean Absolute Error (MAE), and Coefficient of Determination (COD), supplemented by additional metrics. The evaluation aimed to gauge the algorithms' accuracy and predictive capabilities. Notably, for continuous target variables, the gradient-boosting regression model emerged as particularly noteworthy in terms of performance when compared to other models. The gradient-boosting regression model exhibited remarkable results, highlighting its superiority in handling the breast cancer dataset. The model achieved an impressively low MSE value of 0.05, indicating minimal prediction errors. Furthermore, the R-squared value of 0.89 highlighted the model's ability to explain the variance in the data, affirming its robust predictive power. The Mean Absolute Error (MAE) of 0.14 reinforced the model's accuracy in predicting continuous outcomes. Beyond these core metrics, the study incorporated additional measures to provide a comprehensive understanding of the algorithms' performance. The findings underscore the potential of gradient-boosting regression in enhancing predictive accuracy for datasets with continuous target variables, particularly evident in the context of breast cancer diagnosis. This research contributes valuable insights to the ongoing exploration of machine learning algorithms, providing a basis for informed decision-making in medical and predictive analytics domains.