Expert Advice- Disease prediction using Machine Learning

Abstract

Cancer is a type of disease which have now become very frequent and causing very critical damages  in recent years and continue to remain its streak as no reliable cure is found as of now and cure that are found are very costly . This leads to an increase in the number of deaths even if a patient is created with a lot of care and given many therapies.The only way to manage it and reduce its damage is to diagnose it at an early stage. CSE(Comp. Science & Engg.) is used in many  Bioinformatics and Biomedical fields to carry out many analysis ,prediction and to create automated diagnostic tools to ease the manual work of the front line workers that are our Doctors,nurses and group practices and we think it could also be used to diagnose and prognose disease such as Cancer by training the machine in such a way that it could tell the probability of having it in your system. One of the leading fields which could help us in this process is better known as  Machine Learning where various tools and techniques and modules and algorithms  are available to forecast the probability of  cancer on the basis of collected standard data attributes and using them to predict the probability of extent of diseases such as cancer. The Expert Advice is a research paper which implies Machine learning for self-analytics or (analysis through feedback and reward and award techniques) and prediction of illness like cancer and fortify diseases like cancer. In this paper, we have studied ,researched and surveyed various  papers,journals, books and datasets to compare the efficacy of different algos of Machine Learning about cancer prediction based on the given databundel and their attributes like accuracy,probability of prediction comparing different versions of algo and their results..It would be appropriate on our side to state that we can certainly leverage algos like  RandomForest (RF), Naïve Bayes (NB), Decision Tree (DT),SVM, FNN.,RBFN,KNN, etc. In order to reach the desired result of forecasting cancer disease based on the given attributes of data, the best and the most accurate practise and paradigm of computer

networks and application in order to fulfill our goals among all here is SVM, which is Support vector machine algorithm.