Patient-Centric Diagnosis of Acute Leukemia: A Machine Learning Approach Utilizing Flow Cytometry Data

As cancer incidence rises due to lifestyle factors, accurate and cost-effective leukemia diagnosis is crucial in medical diagnostics. Early identification of malignant leukocytes remains challenging, prompting this research to employ machine learning (ML) techniques. Using a flow cytometry test of blood cells which including Forward Scatter Signal, Forward Scatter Pulse Width Signal, Side Scatter Signal, and Side Fluorescence Signal, various ML algorithms (SVM, K-Nearest Neighbor, AdaBoost Algorithm, Logistic Regression, Decision Tree, Random Forest) are applied for individual patient diagnosis. This study underscores the significance of ML in processing leukemia flow cytometry test signals, enhancing accuracy, reducing diagnosis time, and offering cost-effective and safer diagnostic services. By utilizing ML-based approaches, clinicians and laboratory experts can potentially enhance the efficiency of Leukemia detection and classification. This article provides an in-depth review of current machine learning models used for detection and classification of Leukemia, highlighting their methodologies for discrete data of flow cytometry test, shedding light on their potential benefits and challenges. Overall, this research contributes to the ongoing efforts to improve Leukemia diagnosis through innovative and advanced computational approaches.