Leveraging Machine Learning for Predictive Healthcare Models

The application of machine learning (ML) in performing accurate diagnoses, treatment optimization, and enhanced patient care has transformed it into a mainstay of the healthcare system in recent years. This chapter discusses the application of ML to assist with blood and rheumatologic disorders, focusing on predictive algorithms intended for personalized early diagnosis, treatment, and management of the disease. Multifactorial and heterogeneous manifestations of some complicated anemia, leukemia, and lymphoma make the diseases difficult to treat. The use of ML techniques such as decision trees, neural networks, and random forests to analyze blood data for predicting disease progression has been carried out. Some models can analyze DNA data, clinical information, and laboratory results to detect certain patterns and indicators of blood disorders. Even though there lies a great deal of possibilities with ML in hematology and rheumatology, there are considerable challenges regarding the quality of data, model explainability, clinical relevance, and integration. For adaptable ML frameworks that can be used without modification across diverse populations, these gaps in the literature pose urgent concerns.