ANNCRIPS: Artificial Neural Networks for Cancer Research In Prediction & Survival

TL;DR

This paper introduces ANNCRIPS, an artificial neural network-based model aimed at improving early prostate cancer detection by reducing false positives and supporting healthcare decision-making.

Contribution

The study presents a novel ANN model specifically designed for prostate cancer prediction, enhancing early detection accuracy over traditional methods.

Findings

The model shows promising potential in reducing false positives.

Initial validation indicates improved detection accuracy.

The approach can be integrated into clinical screening processes.

Abstract

Prostate cancer is a prevalent malignancy among men aged 50 and older. Current diagnostic methods primarily rely on blood tests, PSA:Prostate-Specific Antigen levels, and Digital Rectal Examinations (DRE). However, these methods suffer from a significant rate of false positive results. This study focuses on the development and validation of an intelligent mathematical model utilizing Artificial Neural Networks (ANNs) to enhance the early detection of prostate cancer. The primary objective of this research paper is to present a novel mathematical model designed to aid in the early detection of prostate cancer, facilitating prompt intervention by healthcare professionals. The model's implementation demonstrates promising potential in reducing the incidence of false positives, thereby improving patient outcomes. Furthermore, we envision that, with further refinement, extensive testing, and validation, this model can evolve into a robust, marketable solution for prostate cancer detection. The long-term goal is to make this solution readily available for deployment in various screening centers, hospitals, and research institutions, ultimately contributing to more effective cancer screening and patient care.