Rationalization of Hematological Analysis of Diseases under Machine Learning Based Classification Modeling Studies

Classification models are an important part of machine learning and are widely used in fields such as text analysis and medical diagnosis. This paper reviews the current popular classification algorithms, including BP neural network, support vector machine, random forest, particle swarm optimization BP neural network and extreme learning machine. By analyzing the characteristics, advantages and disadvantages, and applicable scenarios of each type of algorithms are in hematological analysis of diseases[1]. We discuss their performance and future development prospects in the field of hematological analysis. Machine learning models can be used as an auxiliary tool to help clinicians make more accurate diagnosis of hematological conditions and improve diagnostic efficiency, which is expected to be widely used in medical experiments in the future.