Implementation of Prediction Model for Medical Diagnosis of Breast Cancer using Machine Learning Algorithms and Classification Techniques

Cancer is the one of the uncured disease in the world, its variants too, among which breast cancer is one spreading over the world in female community. The death rate of this disease was setting a great immutable example day by day. Even best treatment facilities and excellent diagnosis medical equipements doctor are given a challenge in curing of the disease. In this paper we attempted to develop a prediction model to predict breast cancer at early stages by using machine learning algorithms and classification techniques using several machine-learning algorithms that are Random Forest, Naïve Bayes, Support Vector Machines SVM, and K-Nearest Neighbours K-NN, and chose the most effective. The experimental results show that SVM gives the highest accuracy 98.5%. The finding will help to select the best classification machine-learning algorithm for breast cancer prediction. We studied the results with breast cancer dataset. The performance of the diagnosis model is obtained by using methods like classification, accuracy, sensitivity and specificity analysis.