Intelligent Diagnosis of Sickle Cell Anemia in Chronic Diseases Through a Machine Learning Predictive System

Recent technological breakthroughs have considerably addressed the growing demand for private, remote, real-time healthcare services, especially for patients with sickle cell disease (SCD), who need continuous monitoring and follow-up. The integrated healthcare system model proposed in this paper uses machine learning (ML) to improve the seamless, anytime delivery of these services. The system is intended to accurately distinguish SCD patients from healthy individuals using five machine learning algorithms like Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Decision Tree (DT), and Linear Discriminant Analysis (LDA). Experiments with these algorithms demonstrated the accuracy of the SVM approach, with an 84.21% classification accuracy rate. This result not only shows the effectiveness of the SVM algorithm but also the general promise of using ML in healthcare systems to enhance the treatment that patients with chronic illnesses like SCD receive. The suggested model seeks to transform patient care by incorporating these technologies and laying the groundwork for further study and advancement in the area of healthcare management and monitoring.