Breast Cancer Prediction using Machine Learning

With the increase in occurrence of human diseases all over the world, it has created a wide number of opportunities for innovating such kind of disease prediction mechanisms. Out of which, Breast Cancer has grown in tremendous number with an increased amount in the past decade and this has gone increasing till now, & would continue to grow. Now moving further, there is a tremendous need for efficient text analytics tools and feature extraction tools to assist the work related in classifying, sharing and retrieving the information on human diseases in general and in the field of Breast Cancer also. In addition to that, the present study has been made with the objective to provide a complete analysis of different classifiers on Breast Cancer dataset, and to generate a new ensemble training method/model of Machine Learning Classification. Here, machine learning algorithms (such as Decision Tree , Logistic Regression ,Support Vector Machine, Random Forest). An Ensemble Learning model for Prediction is proposed to classify the results among different classifiers as mentioned in the above statement. At the end, the Voting Ensemble technique is implemented in order to find out the optimal classifier that predicts the Breast Cancer. The results have been computed on the basis of evaluation parameters such as Accuracy, Precision, Recall and Specificity. On the other hand, the confusion matrix is measured on the basis of evaluation parameters that generally provides more emphasised, predicted and actual instance of data. Whereas, Performance Evaluation for various machine learning algorithms is then computed. Results of this investigation says that, the Voting Ensemble superpowers other machine learning algorithms.