Cancer Prediction Using Machine Learning Algorithms

The main objective of this project to build the model for predicting cancer using a support vector machine classifier algorithm and compare the accuracies on different kernels and apply the various parameters on the efficient one kernel. The cancer dataset will be imported from the scikit-learn library. Cancer has been characterized as a heterogeneous disease consisting of many various subtypes. The soon diagnosis and prognosis of a cancer type have becomes a need in cancer research, as it can facilitate the subsequent clinical management of patients. these technique include Artificial Neural Network, Bayesian Networks, Support Vector Machines and Decision Trees have been widely apply in cancer research for the development of predictive prototype, results in effective and accurate decision making. Even though it is obvious that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validations is needed in order for these methods to be Consider in the everyday clinical practice. In this work, we present a review of Machine learning approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised Machine learning techniques as well as on different input features and data samples. The using Algorithm KNN (K Nearest Neighbors), SVM (Support Vector Machine), LR (Logistic Regression), NB (Nave Bayes) and also evaluate and compare that the classification of accuracy, precision, recall, f1-score. the UCI machine learning dataset will be partitioned as 75 % for training phase and 25 % for the testing phase and then apply all algorithm is best performance of All parameter.