1. Differential diagnosis of iron deficiency anemia from aplastic anemia using machine learning and explainable Artificial Intelligence utilizing blood attributes.
- Author
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Darshan BSD, Sampathila N, Bairy GM, Prabhu S, Belurkar S, Chadaga K, and Nandish S
- Subjects
- Humans, Diagnosis, Differential, Male, Female, Erythrocyte Count, Erythrocyte Indices, Adult, Hematologic Tests methods, Anemia, Aplastic blood, Anemia, Aplastic diagnosis, Machine Learning, Anemia, Iron-Deficiency diagnosis, Anemia, Iron-Deficiency blood, Artificial Intelligence
- Abstract
As per world health organization, Anemia is a most prevalent blood disorder all over the world. Reduced number of Red Blood Cells or decrease in the number of healthy red blood cells is considered as Anemia. This condition also leads to the decrease in the oxygen carrying capacity of the blood. The main goal of this research is to develop a dependable method for diagnosing Aplastic Anemia and Iron Deficiency Anemia by examining the blood test attributes. As of today, there are no studies which use Interpretable Artificial Intelligence to perform the above differential diagnosis. The dataset used in this study is collected from Kasturba Medical College, Manipal. The dataset consisted of various blood test attributes such as Red Blood cell count, Hemoglobin level, Mean Corpuscular Volume, etc. One of the trending topics in Machine Learning is Explainable Artificial Intelligence. They are known to demystify the machine learning outputs to all its stakeholders. Hence, Five XAI tools including SHAP, LIME, Eli5, Qlattice and Anchor are used to understand the model's predictions. The importance characteristics according to XAI models are PLT, PCT, MCV, PDW, HGB, ABS LYMP, WBC, MCH, and MCHC. are employed to train and test the data. The goal of using data analytic techniques is to give medical professionals a useful tool that improves decision-making, enhances resource management, and eventually raises the standard of patient care. By considering the unique qualities of each patient, medical professionals who must rely on AI-assisted diagnosis and treatment suggestions, XAI offers arguments to strengthen their faith in the model outcomes., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethical approval: Ethical clearance has been obtained to collect patient data from Manipal Academy of Higher Education ethics committee with id IEC1 :229/2022. The need for informed consent was waived by ethics committee/ Institutional Review board of Manipal Academy of Higher Education, because of the retrospective nature of the study. All methods were carried out in accordance with relevant guidelines and regulations., (© 2024. The Author(s).)
- Published
- 2025
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