Enhancing the predictive accuracy of prostate cancer outcomes via a comparative study of k-nearest neighbor and gradient boosting algorithms

In this research paper is to relate effectiveness of KNN algorithm and the Gradient Boost algorithm for editing prostate cancer, in order to determine which one is more efficient. Materials and methods: This study aimed to relate K Nearest Neighbor and Gradient Boost machine learning algorithms for predicting prostate cancer. Each algorithm was run more than ten times, and the top five performing models were recorded for each. The analysis was performed on a sample size of 20, divided into two groups of N=10. Our approach achieved an accuracy rate of over 81%, suggesting potential for developing an effective prostate cancer diagnostic tool. Results and discussion: The suggested machine learning methods have the potential to improve prostate cancer diagnosis and could have a significant impact on patient outcomes. The significant value is p=0.01 which is less than the 0.05. So there is a significant variance between the two sets. Conclusion: The study highlights the significance of accurate prostate cancer prediction for early detection and effective treatment. The research results indicated that the Gradient Boost model achieved superior accuracy of 81% in comparison to KNN, which achieved an accuracy of 66%.