title: ÐÐ½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ Ð¾ Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°Ð½Ð¸ÑÑ ÑÐµÑÐ´ÐµÑÐ½Ð¾-ÑÐ¾ÑÑÐ´Ð¸ÑÑÐ¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ ÑÑÐµÐ´ÑÑÐ²Ð°Ð¼Ð¸ ÑÐ·ÑÐºÐ° R Ð¸ Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÑÐµÑÐºÐ¸Ñ ÑÐ»ÑÐ¶Ð± SQL Server
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Application of data mining methods in heart disease prediction using R tools and SQL Server analysis services
Creators: name: ÐÐ²ÐµÑÐ¸Ð½Ð°, ÐÐ½Ð°ÑÑÐ°ÑÐ¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: ÐÐ°Ð·Ñ Ð´Ð°Ð½Ð½ÑÑ
ÐÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÐµ Ð¼Ð°ÑÐ¸Ð½Ñ ÑÐ»ÐµÐºÑÑÐ¾Ð½Ð½ÑÐµ Ð¿ÐµÑÑÐ¾Ð½Ð°Ð»ÑÐ½ÑÐµ
ÐÐ°ÑÑÐ¸ÑÑ (Ð¼Ð°Ñ.)
ÐÐ°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð°Ð½Ð°Ð»Ð¸Ð·
Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸
ÑÐµÑÐ´ÐµÑÐ½Ð¾-ÑÐ¾ÑÑÐ´Ð¸ÑÑÑÐµ Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°Ð½Ð¸Ñ
classification models
heart disease

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description: The given work is devoted to research efficiency of mining methods in heart disease prediction using R tools and SQL Server analysis services. The research set the following goals: 1. Investigation of data analysis and data mining methods; 2. Studying features of data analysis and storage in R and MS SQL Server 2017; 3. Analysis of subject field and exploratory data analysis using data mining and statistical methods; 4. Building mining models to forecast development of heart disease for a random patient and evaluation of these models. The work was fulfilled by using the heart disease dataset created by Hungarian Institute of Cardiology, Zurich and Basel University Hospitals, V.A. Medical Center in Long Beach and Cleveland Clinic. The key attributes were determined in the research as the result of t- and Ï^2- statistical tests, creation of correlation matrices using Spearmanâs, Pearsonâs and Kendallâs correlation coefficients and visual data analysis. The data mining models were designed on the base of key attributes and decision tree, k-nearest neighbors, naive Bayes and logistic regression mining algorithms. The results of the model based on logistic regression algorithm fit the aim of the research better than other models.