Automatic Blood Disease Identification System Using Machine Learning Algorithms with Hemogram Reports

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Objectives: The primary objective of this research is to create prediction models with machine learning algorithms that can detect blood illnesses in their early stages, including sickle cell disease, leukemia, anemia, and lymphoma. Methods: Reducing the amount of deaths linked to these illnesses and increasing the precision of disease detection. The study used machine learning algorithms: Decision Tree, Random Forest, and XGBoost classifier, and it used a dataset of complete blood count reports. Based on the characteristics of the total blood count, these algorithms were trained on the dataset to forecast the probability of blood diseases. Findings: According to the study, there is a better chance of a cure for blood problems since machine learning models can correctly detect them in their early stages. The findings imply that using predictive models can both lower the death rate and enhance the quality of life for those with these conditions. With a 99.10% accuracy rate, the Random Forest method outperforms other algorithms like Decision Tree, and XGBoost. Novelty: This research is unique because it uses a dataset of complete blood count reports results to use machine learning algorithms for blood condition prediction. Healthcare systems may detect blood problems early on and enhance patient outcomes by using classifier algorithms such as Decision Tree, Random Forest, and XGBoost. This opens up new avenues for illness identification. Keywords: Machine Learning, Decision Tree, Random Forest, XGBoost, Blood Diseases