scholarly journals Peningkatan Akurasi Klasifikasi Backpropagation Menggunakan Artificial Bee Colony dan K-NN Pada Penyakit Jantung

2021 ◽  
Vol 5 (1) ◽  
pp. 208
Author(s):  
Pandito Dewa Putra ◽  
Sukemi Sukemi ◽  
Dian Palupi Rini
Keyword(s):  
Blood Pressure ◽  
Heart Disease ◽  
Artificial Bee Colony ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Backpropagation Algorithm ◽  
Model Accuracy ◽  
Bee Colony ◽  
The World ◽  
K Nearest Neighbor Algorithm

Heart disease has ranked as the leading cause of death in the world, accounting for around 17.3 million deaths per year with some causes, as high blood pressure, diabetes, cholesterol fluctuation, fatigue, and some others which is collected on dataset. Heart disease dataset that was applied is cleveland heart disease with fourteen (14) data atribute. The method that was applied in this research was using Backpropagation algorithm on heart disease classifying, where will be combined Artificial Bee Colony and k-Nearest Neighbor algorithm for features or atribute choose due to this technique can increase classifier model accuracy which is produced as much as 94,23%.

scholarly journals Heart disease prediction model with k-nearest neighbor algorithm

2021 ◽  
Vol 10 (3) ◽  
pp. 225
Author(s):  
Tssehay Admassu Assegie
Keyword(s):  
Heart Disease ◽  
Prediction Model ◽  
Nearest Neighbor ◽  
Predictive Performance ◽  
Data Repository ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Proposed Model ◽  
K Nearest Neighbor Algorithm ◽  
Learning Data

<span>In this study, the author proposed k-nearest neighbor (KNN) based heart disease prediction model. The author conducted an experiment to evaluate the performance of the proposed model. Moreover, the result of the experimental evaluation of the predictive performance of the proposed model is analyzed. To conduct the study, the author obtained heart disease data from Kaggle machine learning data repository. The dataset consists of 1025 observations of which 499 or 48.68% is heart disease negative and 526 or 51.32% is heart disease positive. Finally, the performance of KNN algorithm is analyzed on the test set. The result of performance analysis on the experimental results on the Kaggle heart disease data repository shows that the accuracy of the KNN is 91.99%</span>

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

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

scholarly journals CLASSIFICATION OF KIDNEY DISEASE USING GENETIC MODIFIED KNN AND ARTIFICIAL BEE COLONY ALGORITHM

SINERGI ◽  
2021 ◽  
Vol 25 (2) ◽  
pp. 177
Author(s):  
Ardina Ariani ◽  
Samsuryadi Samsuryadi
Keyword(s):  
Chronic Kidney Disease ◽  
Feature Selection ◽  
Kidney Disease ◽  
Artificial Bee Colony ◽  
Nearest Neighbor ◽  
Kidney Diseases ◽  
K Nearest Neighbor ◽  
Abc Algorithm ◽  
Bee Colony

The health care system is currently improving with the development of intelligent artificial systems in detecting diseases. Early detection of kidney disease is essential by recognizing symptoms to prevent more severe damages. This study introduces a classification system for kidney diseases using the Artificial Bee Colony (ABC) algorithm and genetically modified K-Nearest Neighbor (KNN). ABC algorithm is used as a feature selection to determine relevant symptoms used in influencing kidney disease and Genetic modified KNN used for classification. This research consists of 3 stages: pre-processing, feature selection, and classification. However, it focuses on the pre-processing stage of chronic kidney disease using 400 records with 24 attributes for the feature selection and classification. Kidney disease data is classified into two classes, namely chronic kidney disease and not chronic kidney disease. Furthermore, the performance of the proposed method is compared with other methods. The result showed that an accuracy of 98.27% was obtained by dividing the dataset into 280 training and 120 test data.

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%.

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