Using machine learning algorithms for processing medical data

Abstract

The paper considers the comparative analyses of machine learning algorithms for dataset: cardio_train.csv from kaggle.com (link: https://www.kaggle.com/sulianova/cardiovascular-disease-dataset). Moreover, using machine learning algorithms there will be discovered the best accuracy algorithms for the cardio_train.csv. Considering procedures have done in Python 3.0 programming language, which represents confusion matrix and classification report, in order to see precision score, recall, f1-score, and support. Furthermore, in this paper you are able to see following classification models: KNN algorithm, Logistic Regression, Decision Tree, Random Forest, Naïve Bayes and SVM. As a result, it will be defined the superior accuracy for processing medical dataset.