Yazar "Ashok Kumar, Saranya" seçeneğine göre listele

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    Comparison of machine learning models for lung cancer prediction using different feature selection methodologies
    (Elsevier, 2024) Bai, Femilda Josephin Joseph Shobana; Aruna, S; Ashok Kumar, Saranya; Maheswari, M; Katyal, Krish; Vipat, Dhaivat; Parasar, Sanjeeban
    Lung cancer is one of the most widespread diseases with significant fatality rates worldwide. Machine learning (ML) algorithms have recently demonstrated great promise for predicting lung cancer. The proposed research focuses on the extraction of helpful features from patient data that can enhance the precision and understandability of machine learning models. Correlation-based feature selection, recursive feature elimination (RFE), and tree-based feature selection are examined to see which is the most effective feature selection technique for predicting lung cancer. The ML models Naive Bayes (NB), K-nearest neighbor (KNN), and support vector machines (SVMs) were trained to produce predictions using the chosen features. Accuracy, precision, recall, and F1-score metrics were used to assess the model's performance. When the models were trained with and without feature selection, KNN and SVM displayed the highest prediction accuracy compared with NB. One of the feature selection techniques that has helped machine learning models to be trained with the most relevant attributes, increasing prediction accuracy, is tree-based feature selection. © 2024 Elsevier Inc. All rights reserved.
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    Early detection of cardiovascular disease: Data visualization, feature selection, and machine learning algorithms for predictive diagnosis
    (Elsevier, 2024) Bai, Femilda Josephin Joseph Shobana; Ashok Kumar, Saranya; Maheswari M.; Aruna S. b; Krishnan, Aditya; Majid, Amaan
    Accurate and early diagnosis of cardiovascular disease is a big concern to improve the patient's well-being. The proposed research is focused toward the prediction of cardiovascular disease using a diversified dataset, which includes the patient's health history and diagnostic test results. The study focuses mainly on data visualization, feature selection, and predictive modeling. To identify the distribution of the features and the relationship between the features, data visualization was performed by using various plots and graphs. The important features in the dataset that can be helpful for better prediction are selected using embedded-based feature selection approach. The prediction of disease utilized machine learning (ML) techniques, including logistic regression (LR), decision trees (DTs), support vector machines (SVMs), and k-nearest neighbors (KNNs). F-1 score, precision, recall, accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves are useful metrics for assessing how well machine learning models perform at predicting disease. These metrics provide insights into the advantages and drawbacks of the models, helping researchers to understand their effectiveness and suitability for specific tasks. 0.98, 0.82, 0.80, and 0.78 are the accuracies, and 0.99, 0.94, 0.89, and 0.83 are the area under the ROC curve (AUC) values of DT, KNN, SVM, and LR predictive models, respectively. The findings provide an insight to the healthcare professionals and researchers to understand the usefulness of predictive modeling for early predictions of cardiovascular disease. © 2024 Elsevier Inc. All rights reserved.