Enhancing Early Detection of Blood Disorders through A Novel Hybrid Modeling Approach

Abstract

Blood disorders are such conditions that impact the blood’s ability to function correctly. There is a range of different symptoms depending on the type. There are several different types of blood disorders such as Leukemia, chronic myelocytic leukemia, lymphoma, myelofibrosis, polycythemia, thrombocytopenia, anemia, and leukocytosis. Some resolve completely with therapy or do not cause symptoms and do not affect overall lifespan. Some are chronic and lifelong but do not affect how an individual lives. Other blood disorders, like sickle cell disease and blood cancers, can be even fatal. There needs to be a capture of hidden information in the medical data for detecting diseases in the early stages. This paper presents a novel hybrid modeling strategy that makes use of the synergy between two methods with histogram-based gradient boosting classifier tree and random subspace. It should be emphasized that the combination of these two models is being employed in this study for the first time. We present this novel model built for the assessment of blood diseases. The results show that the proposed model can predict the tumor of blood disease better than the other classifiers.

Keywords

• applied statistic
• statistical analysis and application
• structural and functional data
• machine learning.

References

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