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Leukemia, the cancer of blood cells, originates in the blood-forming cells ofthe bone marrow. In Chronic Myeloid Leukemia (CML) conditions, the cellspartially become mature that look like normal white blood cells but do notresist infection effectively. Early detection of CML is important for effectivetreatment, but there is a lack of routine screening tests. Regular check-upsand monitoring of symptoms are the best way to detect CML in the early stages.In the study, we developed a multi-layer-perception-based meta-ensemble systemusing protein amino acid sequences for early risk prediction of CML. Thedeleterious mutation analysis of protein sequences provides 7discriminantinformation in amino acid sequences causing CML. The protein sequences areexpressed into molecular descriptors using the values of hydrophobicity andhydrophilicity of the amino acids. 9 These descriptors are transformed invarious statistical and correlation-based feature spaces. These 10 featuresinformation is given to several diverse types of base learners. The preliminarypredictions of 11 base-learners are employed to develop Multi-LayeredPerceptron (MLP) based meta-ensemble. The 12 proposed learning approacheffectively utilizes the discriminant information to classify CML/non- 13 CMLprotein sequences. The proposed prediction system has given improved resultsand it can be 14 employed as a potential biomarker for early diagnosis of CML.

Early Risk Prediction of Chronic Myeloid Leukemia with Protein Sequences using Machine Learning-based Meta-Ensemble