Anemia Diagnosis And Prediction Based On Machine Learning

Shehab, Sara A; Shehab, Eman; Khawaga, AbdulRahman

doi:10.21608/kjis.2023.220945.1014

Anemia Diagnosis And Prediction Based On Machine Learning

Document Type : Original Article

Authors

¹ Computer Science Dep, Faculty of Computer and Artificial Intelligence, Sadat city, Egypt

² Department of computer science, faculty of computers and artificial intellgence, sadat city

³ student bioinformatic department, faculty of computers and artificial intellgence, sadat city

10.21608/kjis.2023.220945.1014

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