Clean medical data and predict heart disease

Basit Öğe Kaydı
dc.contributor.authorAlkhafaji, Mohammed Jasim A.
dc.contributor.authorAljuboori, Abbas Fadhil
dc.contributor.authorIbrahim, Abdullahi Abdu
dc.date.accessioned2021-05-15T12:49:44Z
dc.date.available2021-05-15T12:49:44Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2020 -- 26 June 2020 through 27 June 2020 -- -- 162106
dc.description.abstractThe enormous data provided by the health care environment needs many important and powerful tools for analyzing and extracting data and accessing useful knowledge. Many researchers have been interested in applying many statistical tools as well as many different data mining tools in order to improve an analysis process and extract data from a different data set. The only thing that proves the success and robustness of data mining tool is accurate diagnosis of the disease. According to the (WHO), the biggest cause of death in the last ten years or so in this vast world is heart disease. The statistical exploration tools that researchers use are tools that help decision-makers in health care to predict and diagnose heart disease. The tools used in the diagnostic process for heart disease have been thoroughly tested in order to demonstrate sufficient and acceptable accuracy. A set of patient data divided into 665 records was used, of which 300 were for males, with 365 for females, with 10 different related characteristics. The decision-making department still suffers from a lack of performance and decision-making. Our paper aims to process data in different ways before the process of accessing knowledge to make the appropriate decision through expectations of classification analysis and then using techniques to extract data with acceptable accuracy. Our goal proposed in this paper is to purify the data before the disease prediction process to get the best possible prediction and compare the results with the results of a group of previous researchers to reach an accurate diagnosis and prediction. The second part of our goal is to compare between different technologies on different data sets such as decision tree technology and the second technique is Bayesian classification technology and the last technology is neural networks and the results were (98.85%, 98.16%, 91.31%), respectively. In the end, we hope to obtain acceptable results with high accuracy in the future, enhance clinical diagnosis, and promote appropriate decision-making for early treatment specialists. © 2020 IEEE.en_US
dc.identifier.doi10.1109/HORA49412.2020.9152870
dc.identifier.isbn9781728193526
dc.identifier.scopus2-s2.0-85089705918
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/HORA49412.2020.9152870
dc.identifier.urihttps://hdl.handle.net/20.500.12939/1105
dc.identifier.wosWOS:000644404300004
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorIbrahim, Abdullahi Abdu
dc.institutionauthorAlkhafaji, Mohammed Jasim A.
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofHORA 2020 - 2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectData Miningen_US
dc.subjectDecision Tree Classifieren_US
dc.subjectHeart Disease Predictionen_US
dc.subjectNaïve Bayes Classifieren_US
dc.subjectNeural Networken_US
dc.titleClean medical data and predict heart disease
dc.typeConference Object

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Bilgisayar Mühendisliği Bölümü Koleksiyonu
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