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Title: :  Aniemia detection using machine learning
PaperId: :  23555
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 2 2024
DUI:    16.0415/IJARIIE-23555
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Chaithanya K SDon Bosco Institute of Technology
Deepika M LDon Bosco Institute of Technology
Harshitha MDon Bosco Institute of Technology
Hemavathi SDon Bosco Institute of Technology
Divyashree KDon Bosco Institute of Technology
Yashodara RDon Bosco Institute of Technology

Abstract

Information science and engineering
Anemia, Hemoglobin, MCV, MCH, MCHC, Machine Learning, Logistic regression, K Nearest Classifier, Random Forest, Decision Tree,Artificial neural network.
Computer-aided diagnosis of diseases proves to be a cost- effective solution. In addition to saving time, this approach also ensures accuracy, eliminating for additional manpower in medical decision-making processes. Variousnutrition surveys indicate that nearly a quarter of the global population suffers from anemia. Therefore, there is an urgent need. to develop a proficient machine learning classifier capable of accurately detecting and classifyinganemia. In this study, five ensemble learning methods - Stacking, Bagging, Voting, Adaboost, and Bayesian Boosting- are applied to four classifiers: DT, ANN, Naïve Bayes, and K-Nearest Neighbor. The objective is to identify which individual classifier or combination of classifiers achieves the very best accuracy in classifying blood cells for anemia detection The results demonstrate that among the ensemble methods, the stacking ensemble method attains the highest accuracy. Among the individual classifiers, the ANN performs the best while the K-Nearest Neighbor performs the worst. Interestingly, the combination of T and K-NN, when applied in the Stacking ensemble, achieves substantially better accuracy than the Artificial Neural Network alone. This highlights the reality that an ensemble of classifiers yields superior accuracy as compared to individual classifiers. Therefore, to ensure maximum accuracy in medical decision-making, an ensemble of classifiers should be utilized.

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IJARIIE Chaithanya K S, Deepika M L, Harshitha M, Hemavathi S, Divyashree K, and Yashodara R. "Aniemia detection using machine learning" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 5413-5419
MLA Chaithanya K S, Deepika M L, Harshitha M, Hemavathi S, Divyashree K, and Yashodara R. "Aniemia detection using machine learning." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 5413-5419.
APA Chaithanya K S, Deepika M L, Harshitha M, Hemavathi S, Divyashree K, & Yashodara R. (2024). Aniemia detection using machine learning. International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 5413-5419.
Chicago Chaithanya K S, Deepika M L, Harshitha M, Hemavathi S, Divyashree K, and Yashodara R. "Aniemia detection using machine learning." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 5413-5419.
Oxford Chaithanya K S, Deepika M L, Harshitha M, Hemavathi S, Divyashree K, and Yashodara R. 'Aniemia detection using machine learning', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 5413-5419. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/Aniemia_detection_using_machine_learning_ijariie23555.pdf (Accessed : ).
Harvard Chaithanya K S, Deepika M L, Harshitha M, Hemavathi S, Divyashree K, and Yashodara R. (2024) 'Aniemia detection using machine learning', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 5413-5419IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/Aniemia_detection_using_machine_learning_ijariie23555.pdf (Accessed : )
IEEE Chaithanya K S, Deepika M L, Harshitha M, Hemavathi S, Divyashree K, and Yashodara R, "Aniemia detection using machine learning," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 5413-5419, Mar-App 2024. [Online]. Available: http://ijariie.com/AdminUploadPdf/Aniemia_detection_using_machine_learning_ijariie23555.pdf [Accessed : ].
Turabian Chaithanya K S, Deepika M L, Harshitha M, Hemavathi S, Divyashree K, and Yashodara R. "Aniemia detection using machine learning." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 ().
Vancouver Chaithanya K S, Deepika M L, Harshitha M, Hemavathi S, Divyashree K, and Yashodara R. Aniemia detection using machine learning. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(2) : 5413-5419. Available from: http://ijariie.com/AdminUploadPdf/Aniemia_detection_using_machine_learning_ijariie23555.pdf
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