Anemia Diagnosis And Prediction Based On Machine Learning
shehab, S., Shehab, E., Khawaga, A. (2023). Anemia Diagnosis And Prediction Based On Machine Learning. EKB Journal Management System, 4(2), 1-9. doi: 10.21608/kjis.2023.220945.1014
Sara A shehab; Eman Shehab; AbdulRahman Khawaga. "Anemia Diagnosis And Prediction Based On Machine Learning". EKB Journal Management System, 4, 2, 2023, 1-9. doi: 10.21608/kjis.2023.220945.1014
shehab, S., Shehab, E., Khawaga, A. (2023). 'Anemia Diagnosis And Prediction Based On Machine Learning', EKB Journal Management System, 4(2), pp. 1-9. doi: 10.21608/kjis.2023.220945.1014
shehab, S., Shehab, E., Khawaga, A. Anemia Diagnosis And Prediction Based On Machine Learning. EKB Journal Management System, 2023; 4(2): 1-9. doi: 10.21608/kjis.2023.220945.1014

Anemia Diagnosis And Prediction Based On Machine Learning

Kafrelsheikh Journal of Information Sciences
Article 1, Volume 4, Issue 2, November 2023, Page 1-9  XML PDF (997.44 K)
Document Type: Original Article
DOI: 10.21608/kjis.2023.220945.1014
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Authors
Sara A shehab email 1; Eman Shehab2; AbdulRahman Khawaga3
1Computer Science Dep, Faculty of Computer and Artificial Intelligence, Sadat city, Egypt
2Department of computer science, faculty of computers and artificial intellgence, sadat city
3student bioinformatic department, faculty of computers and artificial intellgence, sadat city
Abstract
The extraordinary developments in the health sector have resulted in the substantial production of data in daily life. To get valuable information out of this data—information that can be used for analysis, forecasting, making suggestions, and making decisions—it must be processed. Accessible data is converted into useful information using data mining and machine learning approaches. The first challenge for medical practitioners in developing a preventative strategy and successful treatment plan is the timely diagnosis of diseases. Sometimes, this can result in death if accuracy is lacking. In this study, we examine supervised machine learning methods (Decision Tree, Multilayer Perceptron “MLP”, K-nearest neighbors “KNN”, Logistic Regression, Random Forest, and Support Vector Machine “SVC”) for anemia prediction utilizing CBC (Complete Blood Count) data gathered from pathology labs. The outcomes demonstrate that the Random Forest, Multilayer Perceptron “MLP”, Decision Tree, and Logistic Regression techniques outperform KNN and SVC in terms of accuracy of 99.94%.
Keywords
Haemoglobin; Random Forest; Decision Tree; Logistic Regression; multilayer perceptron
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