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<p>Breast Cancer (BC) is a crucial disease among all types of cancers, accounting for almost 15 percent of all cancer mortalities. To reduce such a vast mortality rate, early detection of the disease is essential. A fast, accurate, and interpretable machine learning model is a research subject. Fewer features reduce the computational effort and improve interpretation. A 3-Phase Hybrid Filter-Wrapper feature selection approach and a Stacked Classification model is evaluated on the Wisconsin breast cancer (WBC) dataset with 30 features and one outcome variable and chronic kidney disease (CKD) dataset available in UCI learning repository with 24 independent and 1 dependent features. Phase 1 uses a Greedy step-wise search and selects 11 features well correlated with the class but not among themselves. Phase 2 utilizes a Best-first search and Logistic regression learning algorithm to select six (WBC dataset) and five (CKD dataset) features respectively. In Phase-3, Logistic Regression (LR), Naïve Bayes (NB), Hoeffding Tree (HT), Support Vector Machine (SVM) with the polynomial kernel, Multilayer Perceptron Network (MLN), and the Stacked model are used with the six and five features to identify patients with or without BC/ CKD. The Stacked model uses three RF as Base-classifiers and MLP as the Meta-classifier. Data splitting, several metrics, and statistical tests are used, along with 10-Fold cross-validation, to do a comparative analysis. LR, NB, and HT demonstrate improvement across performance measures on reducing the features to six/ five. In the 50–50 split, SVM with 30(WBC dataset) and 24(CKD dataset) features, and MLP &amp; LR with six/ five features, record higher than 98% accuracy. The Stacked model records 100% accuracy with six/ five features and 50–50, 66–34 &amp; 80–20 splits along with tenfold cross validations. These values and feature reduction improves upon previous studies.</p>

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Medical Diagnosis Through Improved Feature Selection and Advanced Ensemble Techniques: A Study on Breast Cancer and Chronic Kidney Disease

  • 期刊论文

  • 发表日期：

    2026年2月19日

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  • Sulekha Das,
  • Avijit Kumar Chaudhuri,
  • Partha Ghosh

摘要

Breast Cancer (BC) is a crucial disease among all types of cancers, accounting for almost 15 percent of all cancer mortalities. To reduce such a vast mortality rate, early detection of the disease is essential. A fast, accurate, and interpretable machine learning model is a research subject. Fewer features reduce the computational effort and improve interpretation. A 3-Phase Hybrid Filter-Wrapper feature selection approach and a Stacked Classification model is evaluated on the Wisconsin breast cancer (WBC) dataset with 30 features and one outcome variable and chronic kidney disease (CKD) dataset available in UCI learning repository with 24 independent and 1 dependent features. Phase 1 uses a Greedy step-wise search and selects 11 features well correlated with the class but not among themselves. Phase 2 utilizes a Best-first search and Logistic regression learning algorithm to select six (WBC dataset) and five (CKD dataset) features respectively. In Phase-3, Logistic Regression (LR), Naïve Bayes (NB), Hoeffding Tree (HT), Support Vector Machine (SVM) with the polynomial kernel, Multilayer Perceptron Network (MLN), and the Stacked model are used with the six and five features to identify patients with or without BC/ CKD. The Stacked model uses three RF as Base-classifiers and MLP as the Meta-classifier. Data splitting, several metrics, and statistical tests are used, along with 10-Fold cross-validation, to do a comparative analysis. LR, NB, and HT demonstrate improvement across performance measures on reducing the features to six/ five. In the 50–50 split, SVM with 30(WBC dataset) and 24(CKD dataset) features, and MLP & LR with six/ five features, record higher than 98% accuracy. The Stacked model records 100% accuracy with six/ five features and 50–50, 66–34 & 80–20 splits along with tenfold cross validations. These values and feature reduction improves upon previous studies.