Predicting cancer risk using machine learning on lifestyle and genetic data.

ABSTRACT: Cancer remains one of the leading causes of mortality worldwide, where early detection significantly improves patient outcomes and reduces treatment burden. This study investigates the application of Machine Learning (ML) techniques to predict cancer risk based on a combination of genetic and lifestyle factors. A structured dataset of 1,200 patient records was used, comprising features such as age, gender, Body Mass Index (BMI), smoking status, alcohol intake, physical activity, genetic risk level, and personal history of cancer. A full end-to-end ML pipeline was implemented, encompassing data exploration, preprocessing, feature scaling, model training, and evaluation using stratified cross-validation and a separate test set. Nine supervised learning algorithms were evaluated and compared,

SUBMITTER: Ahmed MA 

PROVIDER: S-EPMC12365227 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature