Acute Lymphoblastic Leukemia (ALL) is a blood cell cancer characterized by numerous immature lymphocytes. Even though automation in ALL prognosis is an essential aspect of cancer diagnosis, it is challenging due to the morphological correlation between malignant and normal cells. The traditional ALL classification strategy demands experienced pathologists to carefully read the cell images, which is arduous, time-consuming, and often suffers inter-observer variations. This article has automated the ALL detection task from microscopic cell images, employing deep Convolutional Neural Networks (CNNs). We explore the weighted ensemble of different deep CNNs to recommend a better ALL cell classifier.The weights for the ensemble candidate models are estimated from their corresponding metrics, such as accuracy, F1-score, AUC, and kappa values. Various data augmentations and pre-processing are incorporated for achieving a better generalization of the network. We utilize the publicly available C-NMC-2019 ALL dataset to conduct all the comprehensive experiments. Our proposed weighted ensemble model, using the kappa values of the ensemble candidates as their weights, has outputted a weighted F1-score of 88.6 %, a balanced accuracy of 86.2 %, and an AUC of 0.941 in the preliminary test set. The qualitative results displaying the gradient class activation maps confirm that the introduced model has a concentrated learned region. In contrast, the ensemble candidate models, such as Xception, 1
Measles is one of the significant public health issues responsible for the high mortality rate around the globe, especially for developing countries. Using nationally representative demographic and health survey data, measles vaccine utilization has been classified, and its underlying factors are identified through an ensemble Machine Learning (ML) approach. Firstly, missing values are imputed employing various approaches, and then several feature selection techniques have been applied to identify the crucial attributes for predicting measles vaccination. A grid search hyperparameter optimization technique has been applied for tuning the critical hyperparameters of different ML models, such as Naive Bayes, random forest, decision tree, XGboost, and lightgbm. The categorization performance of the individual optimized ML model as all as their ensembles have been reported utilizing our proposed BDHS dataset. Individually, the optimized lightgbm provides the highest precision and AUC of 79.90 % and 77.80 %, respectively. This result improved when the optimized lightgbm is ensembled with XGboost, providing the precision and AUC of 84.60 % and 80.0 %, respectively. Our result reveals that the statistical median imputation technique with the XGboost-based attribute selection method and the lightgbm classifier provides the best individual result. The performance has been improved when the proposed weighted ensemble of the XGboost and lightgbm approach has been adapted with the same preprocessing and recommended for measles vaccine utilization. The significance of our proposed approach is that it utilizes minimum attributes collected from the child and their family members and yielded 80.0 % accuracy, making it easily explainable by caregivers and healthcare personnel. Finally, our predictive model provides an early detection procedure to help national policymakers enforce new policies with specific rules and regulations. The data and source codes that support the findings of this study are available at https://github.com/kamruleee51/measles_vaccine_uptake.
INDEX TERMSAttribute selection, Measles vaccine uptake classification, Measles BDHS data, Missing value imputation, Weighted ensemble ML model.
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