Sickle Cell Anaemia Cell Detection Using Machine Learning Techniques
Vandna Mehndiratta, Anuj Gupta · 2025
The paper presents a comprehensive overview of recent advancements in the field of sickle-shaped cells in the blood leading to a disease commonly known as Sickle Cell anaemia. Machine learning techniques, including classification and clustering algorithms, are employed to analyze complex datasets derived from blood samples, genetic markers, and clinical records. By leveraging these approaches, researchers aim to develop accurate and efficient diagnostic tools capable of identifying individuals with sickle cell anaemia with high sensitivity and specificity. Furthermore, machine learning models facilitate the integration of diverse data sources, enabling a holistic understanding of disease pathophysiology and progression. Challenges such as data heterogeneity, class imbalance, and model interpretability are addressed through innovative algorithmic designs and validation strategies.