Predictive-Probability Analysis and Preventive Strategies for Hemorrhoids:

Abstract

In the world millions of people suffer from hemorrhoids. It is a medical condition that frequently requires prompt intervention for effective management. In this paper, we suggest a machine learning-based method for estimating an individual's risk of developing hemorrhoids. We are using a dataset that includes lifestyle factors, medical history, and demographic data. We use five algorithms: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest, XGBoost and Naive Bayes to create prediction models. Cross-validation and feature selection strategies are applied to improve the validity and performance of the model in this paper through dataset. Our research shows encouraging outcomes in precisely identifying people who are susceptible to hemorrhoids, which will enable early interposition and individualized treatment plans. This work offers a proactive approach to hemorrhoid management and its prevention system, furthering the field of predictive analysis in healthcare. Random Forest’s accuracy 95.19%, KNN’s accuracy is 95.21%, SVM’s accuracy is 95.93%, XGBoosting’s accuracy is 95.32% and Naive Bayes’s accuracy is 90.37. SVM’s training accuracy is the highest and it is 96.67% So, SVM is suitable and best algorithm for predictive-probability analysis and preventive strategies for hemorrhoids. We have 1350 primary data. The dataset consists of 670 of patient records and 680 of normal records. Additionally, a brief summary of preventive strategies intended to lessen the frequency and severity of hemorrhoids is included in our research paper. This paper seeks to equip people with the knowledge and skills needed to reduce the burden of hemorrhoids and improve their overall quality of life by spreading awareness about preventive measures.