GAMA CUTE: Development of a Web-based for Gadjah Mada Caring University for Thalassemia Exit Prediction Tool by Applying Machine Learning

Authors

  • Dimas Chaerul Ekty Saputra Khon Kaen University
  • Afiahayati Afiahayati Gadjah Mada University
  • Tri Ratnaningsih Gadjah Mada University

DOI:

https://doi.org/10.26555/jiteki.v10i3.29301

Keywords:

Anemia, Classification, Machine Learning, Random Forest, K-Nearest Neighbor, GUI

Abstract

Blood disorders occur in one or several parts of the blood that affect the nature and function, and blood disorders can be acute or chronic. Blood disease consists of several types, such as anemia. Anemia is the most common hematologic disorder associated with a decrease in the number of red blood cells or hemoglobin, causing a decrease in the ability of the blood to carry oxygen throughout the body. Patients with anemia in Indonesia have increased for the age of 15-24 years. This study aimed to conduct a screening test for anemia using machine learning. It is expected to know the process of knowing the type of anemia suffered. The machine learning technique used to identify the cause of anemia is divided into four classes, namely Beta Thalassemia Trait, Iron Deficiency Anemia, Hemoglobin E, and Combination (Beta Thalassemia Trait and Iron Deficiency Anemia or Hemoglobin E and Iron Deficiency Anemia). This study would apply the K-Nearest Neighbor (KNN) and Random Forest (RF) methods to build a model on the data collected. The evaluation results using a confusion matrix in the form of accuracy, precision, recall, and f1-score against the KNN and RF methods are 79.36%, 59.40%, 62.80%, and 62.80%. In comparison, the RF is 87.30%, 90.89%, 78.40%, and 81.00%. From the results of comparing the two methods, the Graphic User Interface (GUI) implementation using python applies the RF method. The classifier that gets the highest value among all these parameters is called the best machine learning algorithm to perform screening tests for anemia.

Author Biographies

Dimas Chaerul Ekty Saputra, Khon Kaen University

Department of Computer Science and Information Technology

Afiahayati Afiahayati, Gadjah Mada University

Department of Computer Science and Electronics

Tri Ratnaningsih, Gadjah Mada University

Department of Clinical Pathology and Laboratory Medicine

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Published

2024-11-22

How to Cite

[1]
D. C. E. Saputra, A. Afiahayati, and T. Ratnaningsih, “GAMA CUTE: Development of a Web-based for Gadjah Mada Caring University for Thalassemia Exit Prediction Tool by Applying Machine Learning”, J. Ilm. Tek. Elektro Komput. Dan Inform, vol. 10, no. 3, pp. 649–664, Nov. 2024.

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Vol. 10 No. 3 (2024): September

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Articles

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Copyright (c) 2024 Dimas Chaerul Ekty Saputra, Afiahayati Afiahayati, Tri Ratnaningsih

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