In machine learning, Classification is one of the most important research area. Classification allocates the given input to a known category. In this paper different machine algorithms like Logistic regression (LR), Decision tree (DT), Support vector machine (SVM), K nearest neighbors (KNN) were implemented on UCI breast cancer dataset with preprocessing. The models were trained and tested with k-fold cross validation data. Accuracy and run time execution of each classifier are implemented in python.

Heart and blood vessel disorders are referred to as cardiovascular diseases (CVDs). It is one of the leading global causes of death and consists of many disorders that harm the cardiovascular system. The World Health Organization (WHO) estimates that in 2019, 18 million deaths worldwide were caused by CVDs, accounting for about 32% of all deaths. Therefore, the early detection and prediction of cardiovascular disease can be beneficial in identifying high-risk individuals and enabling timely interventions to reduce the disease’s impact and improve patient outcomes. This study provides a machine learning (ML)-based framework CVD detection to satisfy this criterion. The proposed model includes data preprocessing, hyperparameter optimization using GridSearchCV, and classification using supervised learning approaches, such as support vector machine (SVM), K-nearest neighbors (KNN), XGBoost, random forest (RF), LightBoost (LB), and stochastic gradient descent (SGD). All these models are carried out on the publicly accessed database, namely Kaggle. The experimental results demonstrate that the suggested ML technique has attained a 92.76% detection rate with the SGD classifier on the 80:20 training/testing ratios, which is superior to the well-received approaches.

Breast cancer is a cancerous tumor that arrives within the tissues of the breast. Women are mostly attacked than men. To detect early cancer medical specialists, suggest mammography for screening. Algorithms in Machine learning were executed on mammogram images to classify whether the tissues are deleterious or not. An analysis is done based on the texture feature extraction using different techniques like Frequency decoded local binary pattern (FDLBP), Local Bit-plane Decoded Pattern (LBDP), Local Diagonal Extrema Pattern (LDEP), Local Directional Order Pattern (LDOP), Local Wavelet Pattern (LWP). The features extracted are tested on 322 images from MIA’s database of three different classes. The algorithms in Machine learning like K-Nearest Neighbor classifier (KNN), Support vector classifier (SVC), Decision Tree classifier (DTC), Random Forest classifier (RFC), AdaBoost classifier (AC), Gradient Boosting classifier (GBC), Gaussian Naive Bayes classifier (GNB), Linear Discriminant Analysis classifier (LDA), Quadratic Discriminant Analysis classifier (QDA) were used to evaluate the accuracy of classification. This paper examines the comparison of accuracy using different texture features. KNN algorithm with LDEP for texture feature extraction gives classification accuracy of 64.61%, SVC with all the texture patterns mentioned gives classification accuracy of 63.07%, DTC with FDLBP, LBDP gives classification accuracy of 47.69, RFC with LBDP and AC with LDOP and GBC with FDLBP gives 61.53% classification accuracy, GNB and LDA with FDLBP gives 60% and 63.07% classification accuracy respectively, QDA with LBDP gives 64.61 classification accuracy. Of all the Algorithms support vector classifier gives good accuracy results with all the texture patterns mentioned.