Aniemia detection using machine learning

Chaithanya K S; Deepika M L; Harshitha M; Hemavathi S; Divyashree K; Yashodara R

Aniemia detection using machine learning

April 2024

Vol-10, Issue-2

Paper ID: 23555

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Information science and engineering

Keywords

Anemia Hemoglobin MCV MCH MCHC Machine Learning Logistic regression K Nearest Classifier Random Forest Decision Tree Artificial neural network.

Abstract

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.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	Chaithanya K S	Don Bosco Institute of Technology
2	Deepika M L	Don Bosco Institute of Technology
3	Harshitha M	Don Bosco Institute of Technology
4	Hemavathi S	Don Bosco Institute of Technology
5	Divyashree K	Don Bosco Institute of Technology
6	Yashodara R	Don Bosco Institute of Technology

How to Cite

Use the following formats to cite this article in your research.

APA Style

S, Chaithanya K, L, Deepika M, M, Harshitha, S, Hemavathi, K, Divyashree, & R, Yashodara (2024). Aniemia detection using machine learning. International Journal of Advance Research and Innovative Ideas In Education, 10(2), 5413-5419.

MLA Style

S, Chaithanya K, et al. "Aniemia detection using machine learning." International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 2, 2024, pp. 5413-5419.

IEEE Style

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, 2024.

Vancouver Style

S Chaithanya K, L Deepika M, M Harshitha, S Hemavathi, K Divyashree, R Yashodara. Aniemia detection using machine learning. International Journal of Advance Research and Innovative Ideas In Education. 2024;10(2):5413-5419.

Harvard Style

S, Chaithanya K, L, Deepika M, M, Harshitha, S, Hemavathi, K, Divyashree, & R, Yashodara (2024) 'Aniemia detection using machine learning', International Journal of Advance Research and Innovative Ideas In Education, 10(2), pp. 5413-5419.

Chicago Style

S, Chaithanya K, et al. "Aniemia detection using machine learning." International Journal of Advance Research and Innovative Ideas In Education 10, no. 2 (2024): 5413-5419.

Turabian Style

S, Chaithanya K, et al. "Aniemia detection using machine learning." International Journal of Advance Research and Innovative Ideas In Education 10, no. 2 (2024): 5413-5419.