Pankaj Bhowmik

Work place: Dept. of Computer Science & Engineering, Hajee Mohammad Danesh Science and Technology University, Bangladesh



Research Interests: Artificial Intelligence, Natural Language Processing, Data Mining, Data Compression, Data Structures and Algorithms


Pankaj Bhowmik received B. Sc. (Engg.) degree in Computer Science and Engineering from Hajee Mohammad Danesh Science and Technology University (HSTU), Dinajpur, Bangladesh in 2018. He has prodigious enthusiasm to explore the contemporary horizon of Computer Science fields. His leading research areas are Machine Learning and its real-life applications, Data Science, Natural Language Processing, Artificial Intelligence, Data Mining and Study of sustainable development. He achieved two consecutive Dean's Awards for excellent academic performance while pursuing his Bachelor's degree.

Author Articles
Cardiotocography Data Analysis to Predict Fetal Health Risks with Tree-Based Ensemble Learning

By Pankaj Bhowmik Pulak Chandra Bhowmik U. A. Md. Ehsan Ali Md. Sohrawordi

DOI:, Pub. Date: 8 Oct. 2021

A sizeable number of women face difficulties during pregnancy, which eventually can lead the fetus towards serious health problems. However, early detection of these risks can save both the invaluable life of infants and mothers. Cardiotocography (CTG) data provides sophisticated information by monitoring the heart rate signal of the fetus, is used to predict the potential risks of fetal wellbeing and for making clinical conclusions. This paper proposed to analyze the antepartum CTG data (available on UCI Machine Learning Repository) and develop an efficient tree-based ensemble learning (EL) classifier model to predict fetal health status. In this study, EL considers the Stacking approach, and a concise overview of this approach is discussed and developed accordingly. The study also endeavors to apply distinct machine learning algorithmic techniques on the CTG dataset and determine their performances. The Stacking EL technique, in this paper, involves four tree-based machine learning algorithms, namely, Random Forest classifier, Decision Tree classifier, Extra Trees classifier, and Deep Forest classifier as base learners. The CTG dataset contains 21 features, but only 10 most important features are selected from the dataset with the Chi-square method for this experiment, and then the features are normalized with Min-Max scaling. Following that, Grid Search is applied for tuning the hyperparameters of the base algorithms. Subsequently, 10-folds cross validation is performed to select the meta learner of the EL classifier model. However, a comparative model assessment is made between the individual base learning algorithms and the EL classifier model; and the finding depicts EL classifiers’ superiority in fetal health risks prediction with securing the accuracy of about 96.05%. Eventually, this study concludes that the Stacking EL approach can be a substantial paradigm in machine learning studies to improve models’ accuracy and reduce the error rate.

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