Using Algorithms for the Prediction of Low-risk and High-risk Human Papillomavirus (HPV) in Males

Abstract

This study focus on approximating the prevalence of Human papillomavirus (HPV) among both male staff and students of Ibrahim Badamasi Babangida University in Nigeria, and to analyze both high-risk(hr) and low-risk(lr) factors associated with the persistence of the virus in men which is the leading cause of cervical cancer in women after sexual intercourse. When an HPV-infected man is left untreated for a long period of time, it raises the opposite sex (women) at a future risk of cervical cancer development in the future after the occurrence of sexual intercourse. This paper model high-level python algorithms to categorize, quantify, classify, and predict the male participant(s) who belong to HPV high-risk or low-risk group using Neural Network Algorithm (NNA), Random Forest Algorithm (RFA), and K-Means Clustering Algorithm (KMCA).

This study focus on approximating the prevalence of Human papillomavirus (HPV) among both male staff and students of Ibrahim Badamasi Babangida University in Nigeria, and to analyze both high-risk(hr) and low-risk(lr) factors associated with the persistence of the virus in men which is the leading cause of cervical cancer in women after sexual intercourse. When an HPV-infected man is left untreated for a long period of time, it raises the opposite sex (women) at a future risk of cervical cancer development in the future after the occurrence of sexual intercourse. This paper model high-level python algorithms to categorize, quantify, classify, and predict the male participant(s) who belong to HPV high-risk or low-risk group using Neural Network Algorithm (NNA), Random Forest Algorithm (RFA), and K-Means Clustering Algorithm (KMCA).