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Ensemble Model for the Prediction of Hypertension using KNN and SVM Algorithms

by Saadatu Ali Jijji, Asabe Ahmadu Sandra, Malgwi Yusuf Musa
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 43
Year of Publication: 2021
Authors: Saadatu Ali Jijji, Asabe Ahmadu Sandra, Malgwi Yusuf Musa
10.5120/ijca2021921837

Saadatu Ali Jijji, Asabe Ahmadu Sandra, Malgwi Yusuf Musa . Ensemble Model for the Prediction of Hypertension using KNN and SVM Algorithms. International Journal of Computer Applications. 183, 43 ( Dec 2021), 27-32. DOI=10.5120/ijca2021921837

@article{ 10.5120/ijca2021921837,
author = { Saadatu Ali Jijji, Asabe Ahmadu Sandra, Malgwi Yusuf Musa },
title = { Ensemble Model for the Prediction of Hypertension using KNN and SVM Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2021 },
volume = { 183 },
number = { 43 },
month = { Dec },
year = { 2021 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number43/32221-2021921837/ },
doi = { 10.5120/ijca2021921837 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:19:34.728548+05:30
%A Saadatu Ali Jijji
%A Asabe Ahmadu Sandra
%A Malgwi Yusuf Musa
%T Ensemble Model for the Prediction of Hypertension using KNN and SVM Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 43
%P 27-32
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Hypertension also known as high blood pressure is a dangerous illness because it can lead to strokes, heart disease, heart failure, kidney problem and many more ailment, but when hypertension is detected early it can be prevented or controlled. Thus an intelligent and accurate system is in need for early prediction. Data mining applied to medical field provide innovative results and when two data mining techniques are combined a better performance and more accurate model was developed. A model for the prediction of hypertension in patient using Hybrid data mining technique was developed using hyper-parameter tuning and ensemble method. The model was based on hypertension data set collected from Federal teaching hospital and specialist hospital Gombe state Nigeria. The dataset was further preprocessed and standardized by scaling, fixing missing values and fixing imbalanced data using SMOTE. Grid search technique was using for hyper-parameter tuning. KNN, SVM and Naïve Bayes was used in the model before applying the ensemble technique on KNN and SVM which Gradient Boosting has the accuracy of 0.9985.

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Index Terms

Computer Science
Information Sciences

Keywords

Hypertension Data mining Ensemble technique Hyper-parameter tuning SMOTE Accuracy

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