cancer disease prediction using machine learning big data


"Now a days big data is the fastest and more widely used in every field .With the help of big data medical and health care sectors achieves their growth and with help of big data benefit of an accurate medical data analysis early disease prediction accurate data of an patient can be securely stored and used .Moreover the accuracy of an analysis can be reduced due to an various reason like incomplete medical data some regional disease characteristics which can be outbreaks the prediction. With big data growth in biomedical and healthcare communities accurate analysis of medical data benefits early disease detection patient care and community services. However the analysis accuracy is reduced when the quality of medical data is incomplete. In this paper we can use a machine learning algorithm for the accurate disease prediction for that purpose we can collect the hospital data of a particular region. For missing data we can use latent factor model to achieve the incomplete data. Moreover different regions exhibit unique characteristics of certain regional diseases which may weaken the prediction of disease outbreaks.Machine learning with maximization (support) of separating margin (vector) called support vector machine (SVM) learning is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenetic data the classification feature of SVMs is expanding its use in cancer genomics leading to the discovery of new biomarkers new drug targets and a better understanding of cancer driver genes.Keywords: Machine learning (ML) support vector machine (SVM) classifier Big data analytics Health care data."




machine learning


₹12000 (INR)