Prediction of Prostate Cancer Using Boosting Technique

D.D.V. Sivaram Rolangi; D. Lalitha Bhaskari; Simhadri Venkata Meena

Abstract

There are many diseases that are associated with humans, but some diseases might be associated with only males or only females. This paper shows and discusses the disorder that is associated mainly in men. Prostate cancer is associated with the example of illness. Usually when the damaged cells develop in the prostate gland occurs prostate cancer occurs. These cells increase uncontrollably. Reports given by the researchers show that this is most dangerous disease that is increasing in men day by day. There are different research works done by researchers using different techniques to get a solution for this major problem. As it is a dangerous disorder, medical research is trying hard to improve the prediction of prostate cancer in the field of medical diagnosis. The main aim is to improve the prediction and is still open for contribution. There are many machine learning techniques used by researchers to forecast and solve the issues in the prediction of prostate cancer. This paper approached these challenges by using a new technique in the machine learning boosting techniques by using the algorithm xg boost when compared to the previous algorithms such as support vector machines, random forest, Ada boosting, etc. to predict prostate cancer in men. We assessed our developed model's performance using accuracy as the performance metric, and our findings revealed a predictive accuracy of 99.99%, representing a notable enhancement compared to existing systems.

Abstract

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