Implementation of Artificial Neural Networks and Decision Tree Algorithms for Heart Disease Diagnosis

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dc.contributor.authorBanu, Gul Mohamed Rasithaen_US
dc.contributor.authorBabikar, Thanien_US
dc.contributor.authorBashier, Illhamen_US
dc.contributor.authorSasikala, N.en_US
dc.date.accessioned2020-09-24T07:28:29Z
dc.date.available2020-09-24T07:28:29Z
dc.date.issued2019-02
dc.description.abstractBackground: Various indices derived from red blood cell (RBC) parameters have been described for distinguishing betathalassemia minor and other types of hypochromic microcytic anemia. Objective: The study is aimed at investigating thediagnostic reliability of different RBC indices and formulas in differentiation between beta thalassemia minor and othertypes of hypochromic microcytic anemia. Subjects and Methods: This is a cross‐sectional study which was carried out sincefirst of Jan 2011 to end of December 2011 on 171 children with hypochromic microcytic anemia in Kut Oncology Centre,Wasit, Iraq. Results: There was a statistical significant difference between thalassemic group and other groups regardingblood indices as well as the eight formulas which were used. The highest correctly identified patients (PCIP) was reportedfor RBCs count (84%) with sensitivity and specificity of 96.3%. The Youden's index for RBCs was 58.2 which is the highestvalue compared with other seven parameters or indices which were used in this study. The second highest Youden's indexwas for G & K index, with 78.4% PCIP, and sensitivity and specificity of 98.2%. Youden's index of red cell distributionwidth (RDW) was the lowest value compared to other values used in this study as well as the lowest percentage of correctlyidentified patients (65%). The sensitivity and specificity of RDW for BTM was 86.1%. Conclusion: According to this study,cell counter-based parameters and formulas, particularly RBCs, and Green and King index are superior to all othermethods examined for distinguishing between thalassemia trait and other hypochromic microcytic anemia; while, RDW wasinadequate and ineffective for that purpose.en_US
dc.identifier.affiliationsDepartment of HI, FPHTM, Jazan University, KSAen_US
dc.identifier.affiliationsDepartment of HE, FPHTM, Jazan University, KSAen_US
dc.identifier.affiliationsDepartment of HE, FPHTM, Jazan University, KSAen_US
dc.identifier.affiliationsDepartment of ComputerScience, Md. Sathak College, India.en_US
dc.identifier.citationBanu Gul Mohamed Rasitha, Babikar Thani, Bashier Illham, Sasikala N.. Implementation of Artificial Neural Networks and Decision Tree Algorithms for Heart Disease Diagnosis. International Journal of Medical Research Professionals. 2019 Apr; 8(2): 104-110en_US
dc.identifier.issn2277-3657
dc.identifier.placeIndiaen_US
dc.identifier.urihttps://imsear.searo.who.int/handle/123456789/203627
dc.languageenen_US
dc.publisherInternational Journal of Pharmaceutical Research and Allied Sciencesen_US
dc.relation.issuenumber2en_US
dc.relation.volume8en_US
dc.source.urihttps://ijpras.com/storage/models/article/JDp6bQ3WqE8waQEdHXUMH4p4RmKNSzlN17gIuL823hM2eUsf54aCXxZQPFul/implementation-of-artificial-neural-networks-and-decision-tree-algorithms-for-heart-disease-diagno.pdfen_US
dc.subjectData Miningen_US
dc.subjectHeart Diseaseen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectMultilayer Perceptron (MLP)en_US
dc.subjectDecision Treesen_US
dc.subjectWEKAen_US
dc.subjectAccuracyen_US
dc.titleImplementation of Artificial Neural Networks and Decision Tree Algorithms for Heart Disease Diagnosisen_US
dc.typeJournal Articleen_US
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