Machine Learning Model for Breast Cancer Prediction

Abstract

Genetic mapping is an approach in identifying genes and processes. Genetic maps are essential tools for analyzing DNA sequence data, not only providing a blueprint of the genome but also unlocking linkage patterns between genetic markers, chromosomal regions with more than one sequence variant. Studying these linkage patterns enables diverse applications to identifying the biological underlying feature of problems in health, agriculture, and the study of biodiversity. Genetic mapping provides a mean to understand the basis of genetic and biochemical diseases and provides genetic markers. Mapping studies can be done in a single large pedigree; the larger the number of affected individuals sampled the better the estimate of recombination between the gene causing the disease and one or more nearby genetic marker. This work proposes an algorithm for improving the methods to detect breast cancer by analyzing the DNA data and detect the issue in the DNA samples. This work based on the big data and machine learning techniques to get classifications for all samples. All samples will be classified into two main classes. This work evaluates the performance of different classification algorithms on the dataset. It also provides a website application as the tool that can help specialist predict the of breast cancer based on stated genetic mutation.