Novel prostate cancer detection and classification model using support vector machine

Abstract

Prostate cancer (PCa) is one of the most common and deadliest cancers that kill men worldwide with high mortality and prevalence especially in developed countries. PCa is regarded as one of the most prevalent cancers and is one of the main causes of deaths worldwide. Early detection of PCa diseases helps in making decisions about the progressions that should have occurred in high-risk patients decrease their risks. The recent developments in technology and methods have given rise to computer aided diagnosis (CAD). Early cancer detection can greatly increase the chance of survival through the administration of the proper treatment. Due to the emerging trends and available datasets in state-of-art machine learning (ML) and deep learning (DL) techniques, there has been significant growth in recent disease prediction and classification publications. This paper presents a unique support vector machine-based model for PCa detection and classification. This analysis aims to classify the PCa using ML algorithm and to determine the risk factors. Support vector machines (SVM) is used to identify and classify the PCa. Accuracy, sensitivity, specificity, precision, and F1-score are the measurements used to evaluate the performance of the presented method. This model will achieve accuracy, sensitivity, specificity, precision, and F1-score than earlier models.