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A Novel Ensemble Classifier Framework for Accurate Fetal Heart Rate Classification 4

A Novel Ensemble Classifier Framework for Accurate Fetal Hea...

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4th International Conference on Signal Processing and Communication, ICSPC 2023

作者： Hephzibah, R. Christinal, A. Hepzibah Jayanthi, R. Chandy, D. Abraham Bajaj, Chandrajit Karunya University Department of Mathematics Coimbatore India Karunya University Department of Electronics and Communication Engineering Coimbatore India University of Texas Computational Applied Mathematics Chair in Visualization Austin United States

ISBN: (纸本)9798350300772

This paper presents a study on the prediction of fetal state, which is crucial for preventing fetal mortality. CTG is a technique widely used for monitoring the fetal heart rate and uterine contractions. In our paper, we implemented a novel ensemble classifier that combines the XGBoost and the Random Forest Classifiers to classify the fetal heart rate in CTG data. This study utilized a publicly available dataset with 2126 instances of fetal heart rate and uterine contractions. The results indicate that the ensemble classifier outperforms the individual XGBoost and Random Forest Classifier in terms of precision and F1 score. It also maintains a high level of accuracy of 96% across all categories, indicating that it effectively strikes a balance between correctly identifying true positives while avoiding false positives and negatives. We also compared the results with other classifiers combined with Adaboost and Gradient Boosting techniques and found that our method is more effective than other classifiers. This consistent performance demonstrates the classifier's reliability. © 2023 IEEE.

关键词： Heart

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A Novel Perspective on Brain Tumor Classification Using Hybrid Algorithm

A Novel Perspective on Brain Tumor Classification Using Hybr...

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2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023

作者： Jayanthi, R. Christinal, A. Hepzibah Hephzibah, R. Shekinah, T. Bajaj, Chandrajit Chandy, D. Abraham Karunya University Department of Mathematics Coimbatore India Loyola - Icam College of Engineering and Technology Department of Computer Science and Engineering Chennai India University of Texas Computational Applied Mathematics Chair in Visualization Austin United States Karunya University Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350338003

Brain tumor classification is a critical task in medical imaging, where the early and accurate detection of brain tumors is crucial for patient treatment and survival. In recent years, Machine Learning techniques have emerged as a promising approach for automated brain tumor classification. In this paper, we investigate the performance of three ML classifiers - Support Vector Machine, Naïve Bayes, and an ensemble of SVM and Naïve Bayes - for the two-class classification of brain tumor data into a tumor and no tumor classes. Our experiments demonstrate that the ensemble of SVM and Naïve Bayes classifier achieved an accuracy of 97% outperforming the other classifiers. These results indicate that our ensemble classifier can be a powerful tool for the accurate and efficient classification of brain tumor data, potentially leading to improved patient outcomes. © 2023 IEEE.

关键词： Tumors

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A Novel Ensemble Classifier Framework for Accurate Fetal Heart Rate Classification

A Novel Ensemble Classifier Framework for Accurate Fetal Hea...

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International Conference on Signal Processing and Communication (ICSPC)

作者： R. Hephzibah A. Hepzibah Christinal R. Jayanthi D. Abraham Chandy Chandrajit Bajaj Department of Mathematics Karunya University Coimbatore India Department of Electronics and Communication Engineering Karunya University Coimbatore India Computational Applied Mathematics Chair in Visualization University of Texas Austin

关键词： Heart Fetal heart rate Forestry Signal processing Boosting Reliability Cardiography

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An ensemble technique of SmoteENN and random forest for classification of the cardiotocography data

An ensemble technique of SmoteENN and random forest for clas...

引用

International Conference on Computer Vision and Internet of Things 2023 (ICCVIoT'23)

作者： R. Hephzibah A. H. Christinal R. Jayanthi D. A. Chandy C. Bajaj Department of Mathematics Karunya University Coimbatore India Department of Electronics and Communication Engineering Karunya University Coimbatore India Computational applied Mathematics chair in visualization Institute for Computational Engineering and Sciences University of Texas Austin India

Fetal heart rate monitoring is very important to check the status of the fetal heart during pregnancy. Cardiotocography is the most extensively used technique for the surveillance of the fetal heart rate signal and uterine activity of the mother. It helps in monitoring the health of the fetus in pregnant women at high risk. The various Machine Learning classifiers play a vital role in the classification of the CTG data. The CTG data is the publicly available data with 2126 instances and 21 features which consists of 8 continuous and 13 discrete values. It consists of 1655 normal, 295 suspect, and 176 pathology classes which represents a clear imbalance in the dataset. In this paper, we proposed a novel ensemble method of sampling technique combined with a Random Forest classifier for the classification of the CTG data. There are two sampling techniques such as the oversampling technique to increase the samples in the minority class and the undersampling technique to decrease the samples in the majority class. Here we combined both the techniques such as the SMOTE (Synthetic Minority Oversampling Technique) from the oversampling technique and ENN (Edited Nearest Neighbor) from the undersampling technique and implemented it in the Random Forest classifier for classification. As there is an imbalance in the data, the sampling techniques help to balance the data. This proposed technique achieves the best accuracy of 93% in the classification of the normal, suspect, and pathology data. It also provides better results in terms of other metrics such as sensitivity and specificity. This method is also compared with other competent classifiers and is found to be an effective method for the classification of the CTG data.

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A Novel Perspective on Brain Tumor Classification Using Hybrid Algorithm

A Novel Perspective on Brain Tumor Classification Using Hybr...

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Computer, Electronics & Electrical Engineering & their Applications (IC2E3), International Conference on

作者： R. Jayanthi A. Hepzibah Christinal R. Hephzibah T. Shekinah Chandrajit Bajaj D. Abraham Chandy Department of Mathematics Karunya University Coimbatore India Department of Computer Science and Engineering Loyola – ICAM College of Engineering and Technology Chennai Computational Applied Mathematics Chair in Visualization University of Texas Austin Department of Electronics and Communication Engineering Karunya University Coimbatore India

Brain tumor classification is a critical task in medical imaging, where the early and accurate detection of brain tumors is crucial for patient treatment and survival. In recent years, Machine Learning techniques have emerged as a promising approach for automated brain tumor classification. In this paper, we investigate the performance of three ML classifiers – Support Vector Machine, Naïve Bayes, and an ensemble of SVM and Naïve Bayes – for the two-class classification of brain tumor data into a tumor and no tumor classes. Our experiments demonstrate that the ensemble of SVM and Naïve Bayes classifier achieved an accuracy of 97% outperforming the other classifiers. These results indicate that our ensemble classifier can be a powerful tool for the accurate and efficient classification of brain tumor data, potentially leading to improved patient outcomes.

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