title: ÐÐ½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ Ð¾ ÑÐ°Ñ Ð°ÑÐ½Ð¾Ð¼ Ð´Ð¸Ð°Ð±ÐµÑÐµ ÑÑÐµÐ´ÑÑÐ²Ð°Ð¼Ð¸ ÑÐ·ÑÐºÐ° R
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: Diabetes Data Mining with R
Creators: name: Ð¡Ð°Ð¶Ð½Ð¾Ð²Ð°, ÐÐ¸ÐºÑÐ¾Ñ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: 2023
Subjects: Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð±Ð°Ð»Ð°Ð½ÑÐ¸ÑÐ¾Ð²ÐºÐ° ÐºÐ»Ð°ÑÑÐ°
Ð¼ÐµÑÐ¾Ð´ smote
ÑÐ°Ñ Ð°ÑÐ½ÑÐ¹ Ð´Ð¸Ð°Ð±ÐµÑ
ÑÐ·ÑÐº r
data mining
classification
class balancing
smote method
diabetes
r language

description: Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ â Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÑÐµÐ´ÑÑÐ² ÑÐ·ÑÐºÐ° R Ð´Ð»Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð½Ð°Ð»Ð¸ÑÐ¸Ñ ÑÐ°ÑÐ°ÑÐ½Ð¾Ð³Ð¾ Ð´Ð¸Ð°Ð±ÐµÑÐ° Ð¿Ð¾ ÑÐ¸Ð¼Ð¿ÑÐ¾Ð¼Ð°Ð¼ Ð¸ Ð¾ÑÐ²ÐµÑÐ°Ð¼ Ð¾Ð± Ð¾Ð±ÑÐ°Ð·Ðµ Ð¶Ð¸Ð·Ð½Ð¸ Ð¿Ð°ÑÐ¸ÐµÐ½ÑÐ°. ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°ÑÑÑÑ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: 1. Ð¿ÐµÑÐ²Ð¸ÑÐ½Ð°Ñ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ÑÑÐ¾Ð´Ð½ÑÑ Ð½Ð°Ð±Ð¾ÑÐ¾Ð² Ð´Ð°Ð½Ð½ÑÑ; 2. ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð¸ ÐºÐ¾ÑÑÐµÐ»ÑÑÐ¸Ð¾Ð½Ð½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð½Ð°Ð±Ð¾ÑÐ¾Ð² Ð´Ð°Ð½Ð½ÑÑ; 3. Ð±Ð°Ð»Ð°Ð½ÑÐ¸ÑÐ¾Ð²ÐºÐ° ÐºÐ»Ð°ÑÑÐ¾Ð² Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ Ð¾Ð± Ð¾Ð±ÑÐ°Ð·Ðµ Ð¶Ð¸Ð·Ð½Ð¸ Ð¿Ð°ÑÐ¸ÐµÐ½ÑÐ¾Ð²; 4. Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð´Ð»Ñ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ Ð½Ð°Ð»Ð¸ÑÐ¸Ñ ÑÐ°ÑÐ°ÑÐ½Ð¾Ð³Ð¾ Ð´Ð¸Ð°Ð±ÐµÑÐ° Ð´Ð»Ñ Ð½Ð°Ð±Ð¾ÑÐ° Ñ ÑÐ¸Ð¼Ð¿ÑÐ¾Ð¼Ð°Ð¼Ð¸ Ð¸ Ð´Ð»Ñ Ð½ÐµÑÐ±Ð°Ð»Ð°Ð½ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð¸ Ð´Ð²ÑÑ ÑÐ±Ð°Ð»Ð°Ð½ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ Ð½Ð°Ð±Ð¾ÑÐ¾Ð² Ð¾Ð± Ð¾Ð±ÑÐ°Ð·Ðµ Ð¶Ð¸Ð·Ð½Ð¸ Ð¿Ð°ÑÐ¸ÐµÐ½ÑÐ¾Ð²; 5. ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸, Ð¾ÑÐµÐ½ÐºÐ° ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹; 6. ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ðµ Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿Ð° Ð²ÐµÐ±-Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸ ÑÐ°ÑÐ°ÑÐ½Ð¾Ð³Ð¾ Ð´Ð¸Ð°Ð±ÐµÑÐ° Ð² Ð²Ð¸Ð´Ðµ Ð°Ð½ÐºÐµÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð²Ð¾Ð¿ÑÐ¾ÑÐ¾Ð² Ð¸ÑÑÐ»ÐµÐ´ÑÐµÐ¼ÑÑ Ð½Ð°Ð±Ð¾ÑÐ¾Ð² Ð´Ð°Ð½Ð½ÑÑ. Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ÑÑÐ¾Ð´Ð½ÑÑ Ð½Ð°Ð±Ð¾ÑÐ¾Ð² Ð´Ð°Ð½Ð½ÑÑ Ð½Ð° Ð±Ð°Ð·Ðµ ÑÐ»ÐµÐ´ÑÑÑÐ¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð²: k Ð±Ð»Ð¸Ð¶Ð°Ð¹ÑÐ¸Ñ ÑÐ¾ÑÐµÐ´ÐµÐ¹, Ð´ÐµÑÐµÐ²ÑÐµÐ² ÑÐµÑÐµÐ½Ð¸Ð¹, Ð½Ð°Ð¸Ð²Ð½Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° ÐÐ°Ð¹ÐµÑÐ°, Ð»Ð¾Ð³Ð¸ÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸, ÑÐ»ÑÑÐ°Ð¹Ð½Ð¾Ð³Ð¾ Ð»ÐµÑÐ° Ð¸ XGBoost. ÐÐ»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð½Ð° Ð½Ð°Ð±Ð¾ÑÐµ Ð´Ð°Ð½Ð½ÑÑ Ð¾Ð± Ð¾Ð±ÑÐ°Ð·Ðµ Ð¶Ð¸Ð·Ð½Ð¸ Ð¿Ð°ÑÐ¸ÐµÐ½ÑÐ¾Ð² Ð±ÑÐ»Ð° Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð° Ð±Ð°Ð»Ð°Ð½ÑÐ¸ÑÐ¾Ð²ÐºÐ° ÐºÐ»Ð°ÑÑÐ¾Ð² Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸ RUSE Ð¸ SMOTE. ÐÐ°Ð¸Ð»ÑÑÑÐ¸Ðµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½Ð° Ð±Ð°Ð·Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° XGBoost, Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÐºÐ¾ÑÐ¾ÑÑÑ Ð±ÑÐ» ÑÐ¾Ð·Ð´Ð°Ð½ Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿ Ð²ÐµÐ± Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¿Ð°ÐºÐµÑÐ° Shiny ÑÐ·ÑÐºÐ° R Ð´Ð»Ñ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ°ÑÐ°ÑÐ½Ð¾Ð³Ð¾ Ð´Ð¸Ð°Ð±ÐµÑÐ°.
description: The purpose of the work is the application of data mining methods for detection diabetes by symptoms and answers about the patients lifestyle. Tasks that were solved during the study: 1. primary data processing; 2. statistical and correlation data analysis; 3. class balancing of patientâs lifestyle dataset; 4. building of the diabetes detection models for the symptomâs dataset and for the unbalanced and the two balanced patientâs lifestyle datasets. 5. comparison of the classification results, assessment of the models accuracy; 6. creation of a web application prototype for the diabetes detection, which is the questionnaire based on the questions of the studied data sets. In the work, there are the results of classification by the data mining models based on the following algorithms: k nearest neighbors, decision tree, naive Bayes, logistic regression, random forest and XGBoost algorithms. To improve the accuracy on the patientâs lifestyle dataset, the classes were balanced using the RUSE and SMOTE methods. Models based on the XGBoost algorithm showed the best results on the symptoms dataset and on the balanced dataset using the SMOTE method. Based on the best predictive models, a web application prototype was created for diabetes detection using package Shiny in R.