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

Abstract

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

Efforts of Performance Optimization: The Experiment on Ten Accounting Datasets

“…In addition, [22] observed that the ordinary version classifier in the heart disease diagnosis dataset proved that the level of accuracy had increased after being optimized with the application of the forward selection method from 73.44% to 78.66%. Then, [23] observed that the ordinary version classifier in the Movie Review Polarity V2.0 dataset [36], it was proven that the AUC level had increased after being optimized with the application of the forward selection method from 71.26% to 76.2 %.…”

“…Feature selection is a technique to determine the most relevant attribute in the dataset by selecting the correct subset of the original attributes because not all attributes may be relevant to the problem; even some of these attributes can interfere with the impact at reduced accuracy. In the forward selection method (FS), modeling starts with zero variables (empty model) then the variables are entered one by one until specific criteria are met [21], [22].…”

“…Furthermore, [21] and [22] observe that the forward selection method is feasible to be applied as an optimization effort, whereas [23] also experimented with using the backward elimination method as an optimization effort. The feature selection method, both forward selection and backward elimination, is based on a large feature space reduction, for example, by eliminating irrelevant attributes [23] to increase accuracy [23].…”

Combination of texture feature extraction and forward selection for one-class support vector machine improvement in self-portrait classification

“…This problem can be overcome by minimizing dimensions or irrelevant features, one of which is feature selection. Feature selection can improve machine learning model performance and reduce computation time [21], [22]. In addition, forward selection (FS) is utilized to perform feature selection and results in improved classification performance [22]- [25].…”

Image Classification of Local Robusta and Arabica Coffee Seeds in Malang Regency Using Gray Level Co-Occurrence Matrix and K-Nearest Methods

“…Coffee is one product from current plantation this needed by people around the world. In Indonesia, coffee plantations have very important role for economy national, then Becomes facilitator field work for Public around [1] In Indonesia, especially in Malang Regency, people around many have coffee plantations including namely Robusta coffee and Arabica coffee local. Robusta coffee is type of coffee produced more many compared with arabica coffee.…”