Heart Disease Classification Model Using K-Nearest Neighbor Algorithm

2021 ◽  
Author(s):  
Ben Rahman ◽  
Harco Leslie Hendric Spits Warnars ◽  
Boy Subirosa Sabarguna ◽  
Widodo Budiharto
Keyword(s):  
Heart Disease ◽  
Nearest Neighbor ◽  
Disease Classification ◽  
Classification Model ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm

Implementation of K-Nearest Neighbor Algorithm in Heart Disease Classification

2021 ◽  
Vol 10 (1) ◽  
pp. 39
Author(s):  
Ni Kadek Sukma Putri Rahayu ◽  
I Komang Ari Mogi
Keyword(s):  
Heart Disease ◽  
Nearest Neighbor ◽  
The Body ◽  
Disease Classification ◽  
Training Data ◽  
Main Function ◽  
Performance Measurements ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm

The heart is an important organ that exists in the human body. The main function of the heart is to pump blood throughout the body through blood vessels. The WHO states that as many as 7.3 million people die from heart disease. In this study heart disease will be classified using the K-Nearest Neighbor algorithm. K-Nearest Neighbor algorithm is a classification algorithm based on the distance from data testing against training data with a pre-defined number of k. The results were obtained from performance measurements for the classification of heart disease with the K-Nearest Neighbor algorithm measured using the K-Fold Cross Validation algorithm, from an accuracy rate of 65.89%, a precision level of 66.27%, and a recall of 74.67%.

scholarly journals Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongyan Wang
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Test Score ◽  
Nearest Neighbor ◽  
Classification Model ◽  
Data Mining Algorithm ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Classification Efficiency

This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. Based on the multiple linear regression algorithm, many factors affecting CET4 are analyzed. Ideas based on data mining, collecting history data and appropriate to transform, using statistical analysis techniques to the many factors influencing the CET-4 test were analyzed, and we have obtained the CET-4 test result and its influencing factors. It was found that the linear regression relationship between the degrees of fit was relatively high. We further improve the algorithm and establish a partition-weighted K-nearest neighbor algorithm. The K-weighted K nearest neighbor algorithm and the partition algorithm are used in the CET-4 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET-4 test score, and screen classification is performed to predict when the comparison verification will pass. The weight K of the input feature and the adjacent feature are weighted, although the allocation algorithm of the adjacent classification effect has not been significantly improved, but the stability classification is better than K-nearest neighbor algorithm, its classification efficiency is greatly improved, classification time is greatly reduced, and classification efficiency is increased by 119%. In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated.

scholarly journals Diagnosis Of Heart Disease Using K-Nearest Neighbor Method Based On Forward Selection

2020 ◽  
Vol 4 (2) ◽  
pp. 39-47
Author(s):  
Junta Zeniarja ◽  
Anisatawalanita Ukhifahdhina ◽  
Abu Salam
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Heart Disease ◽  
Early Diagnosis ◽  
Nearest Neighbor ◽  
Selection Method ◽  
K Nearest Neighbor ◽  
Forward Selection ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm

Heart is one of the essential organs that assume a significant part in the human body. However, heart can also cause diseases that affect the death. World Health Organization (WHO) data from 2012 showed that all deaths from cardiovascular disease (vascular) 7.4 million (42.3%) were caused by heart disease. Increased cases of heart disease require a step as an early prevention and prevention efforts by making early diagnosis of heart disease. In this research will be done early diagnosis of heart disease by using data mining process in the form of classification. The algorithm used is K-Nearest Neighbor algorithm with Forward Selection method. The K-Nearest Neighbor algorithm is used for classification in order to obtain a decision result from the diagnosis of heart disease, while the forward selection is used as a feature selection whose purpose is to increase the accuracy value. Forward selection works by removing some attributes that are irrelevant to the classification process. In this research the result of accuracy of heart disease diagnosis with K-Nearest Neighbor algorithm is 73,44%, while result of K-Nearest Neighbor algorithm accuracy with feature selection method 78,66%. It is clear that the incorporation of the K-Nearest Neighbor algorithm with the forward selection method has improved the accuracy result. Keywords - K-Nearest Neighbor, Classification, Heart Disease, Forward Selection, Data Mining

Handwritten Balinesse Character Recognition using K-Nearest Neighbor

2018 ◽  
Author(s):  
I Wayan Agus Surya Darma
Keyword(s):  
Feature Extraction ◽  
Success Rate ◽  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Extraction Process ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Character Feature

Balinese character recognition is a technique to recognize feature or pattern of Balinese character. Feature of Balinese character is generated through feature extraction process. This research using handwritten Balinese character. Feature extraction is a process to obtain the feature of character. In this research, feature extraction process generated semantic and direction feature of handwritten Balinese character. Recognition is using K-Nearest Neighbor algorithm to recognize 81 handwritten Balinese character. The feature of Balinese character images tester are compared with reference features. Result of the recognition system with K=3 and reference=10 is achieved a success rate of 97,53%.

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