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Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach

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Abstract

Pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) technique was analyzed to determine the significance of clinical and phenotypic variables as well as environmental conditions that can identify the underlying causes of child ALL. Fifty pediatric patients (n = 50) included who were diagnosed with acute lymphoblastic leukemia (ALL) according to the inclusion and exclusion criteria. Clinical variables comprised of the blood biochemistry (CBC, LFTs, RFTs) results, and distribution of type of ALL, i.e., T ALL or B ALL. Phenotypic data included the age, sex of the child, and consanguinity, while environmental factors included the habitat, socioeconomic status, and access to filtered drinking water. Fifteen different features/attributes were collected for each case individually. To retrieve most useful discriminating attributes, four different supervised ML algorithms were used including classification and regression trees (CART), random forest (RM), gradient boosted machine (GM), and C5.0 decision tree algorithm. To determine the accuracy of the derived CART algorithm on future data, a ten-fold cross validation was performed on the present data set. The ALL was common in children of age below 5 years in male patients whole belonged to middle class family of rural areas. (B-ALL) was most frequent as compared with T-ALL. The consanguinity was present in 54% of cases. Low levels of platelets and hemoglobin and high levels of white blood cells were reported in child ALL patients. CART provided the best and complete fit for the entire data set yielding a 99.83% model fit accuracy, and a misclassification of 0.17% on the entire sample space, while C5.0 reported 98.6%, random forest 94.44%, and gradient boosted machine resulted in 95.61% fitting. The variable importance of each primary discriminating attribute is platelet 43%, hemoglobin 24%, white blood cells 4%, and sex of the child 4%. An overall accuracy of 87.4% was recorded for the classifier. Platelet count abnormality can be considered as a major factor in predicting pediatric ALL. The machine learning algorithms can be applied efficiently to provide details for the prognosis for better treatment outcome.

Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach.

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Acknowledgements

We are thankful to the Department of Hematology and Oncology, Children Hospital and Institute of Child Health, Lahore for the provision of data.

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Authors and Affiliations

  1. Department of Biochemistry, Human Genetics and Molecular Biology, University of Health Sciences (UHS), Lahore, Pakistan

    Nasir Mahmood

  2. Department of Cell and System Biology, University of Toronto, Toronto, Canada

    Nasir Mahmood

  3. Department of Sciences & Humanities, Foundation for Advancement of Science and Technology (FAST), National University of Computer and Emerging Sciences (NUCES), Lahore, Pakistan

    Saman Shahid & Taimur Bakhshi

  4. The School of Allied Health Sciences, Children’s Hospital and Institute of Child Health, Lahore, Pakistan

    Sehar Riaz & Hafiz Ghufran

  5. Department of Medical Genetics, Children’s Hospital and Institute of Child Health, Lahore, Pakistan

    Muhammad Yaqoob

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  1. Nasir Mahmood
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Correspondence to Nasir Mahmood.

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Mahmood, N., Shahid, S., Bakhshi, T. et al. Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach. Med Biol Eng Comput 58, 2631–2640 (2020). https://doi.org/10.1007/s11517-020-02245-2

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