Abstract: Several diseases are associated with humans; some are synonymous to female and some to male. Example of diseases synonymous to the male gender is Prostate Cancer (PC). Prostate cancer occurs when cells in the prostate gland starts to grow uncontrollably. Statistics shows that prostate cancer is becoming an epidemic among men. Hence, several research works have tried to solve this problem using various methods. Although numerous medical research works are ongoing in the area, the need to introduce technology to battle the epidemic is paramount. Because of this, some researchers have developed several models to help solve issues of prostate cancer in men, but the area is still open to contribution. Recently, some researchers have adopted some well-established Machine Learning (ML) techniques to predict and diagnose the occurrence of prostate cancer, but issues of low prediction accuracy, inability to implement model, low sensitivity; among others still lingers. This paper approached these challenges by developing an ensemble model that combines three (3) ML techniques; Support Vector Machine (SVM), Decision Tree (DT), and Multilayer Perceptron (MP) to predict PC in men. Our developed model was evaluated using sensitivity, specificity and accuracy as performance metrics, and our result showed a prediction accuracy of 99.06%, sensitivity of 98.09% and, specificity of 99.54%, which is a relative improvement on the existing systems.

Oyewo O. A and Boyinbode O.K. “Prediction of Prostate Cancer using Ensemble of Machine Learning Techniques”. International Journal of Advanced Computer Science and Applications (IJACSA) 11.3 (2020). http://dx.doi.org/10.14569/IJACSA.2020.0110318

@article{A2020,
title = {Prediction of Prostate Cancer using Ensemble of Machine Learning Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110318},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110318},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {3},
author = {Oyewo O. A and Boyinbode O.K}
}