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All Journal Jurnal Biometrika dan Kependudukan (Journal of Biometrics and Population)
Sigit Ari Saputro
Faculty of Public Health, Universitas Airlangga, 60115 Surabaya, East Java, Indonesia

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MACHINE LEARNING-BASED PREDICTION OF BREAST CANCER RECURRENCE Nabila Shafiya; Soenarnatalina Melaniani; Ronny Isnuwardana; Sagar Tiwari; Sigit Ari Saputro
Jurnal Biometrika dan Kependudukan Vol. 15 No. 1 (2026): JURNAL BIOMETRIKA DAN KEPENDUDUKAN
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jbk.v15i1.2026.86-96

Abstract

Breast cancer recurrence remains a major cause of mortality among women worldwide. Early identification is essential for improving patient outcomes, and prediction modelling has emerged as an approach to support this objective. This study aimed to develop and compare machine learning models for predicting breast cancer recurrence. A retrospective secondary analysis was conducted on data from 286 patients in the University of California, Irvine (UCI) Machine Learning Repository. Nine predictors were used to develop logistic regression (LR), artificial neural network (ANN), and extreme gradient boosting (XGBoost) models. The dataset was split into training and testing sets using a 70:30 ratio. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), Brier score, and calibration analysis, with 95% confidence intervals estimated through bootstrap resampling. LR demonstrated the best discriminatory performance, achieving a test AUC of 0.790. In contrast, XGBoost and ANN showed lower generalization performance, with test AUCs of 0.727 and 0.748, respectively. LR also achieved the highest testing recall (0.731) and F1-score (0.623), indicating superior sensitivity for identifying recurrent cases. Meanwhile, XGBoost demonstrated the highest precision (0.727), accuracy (0.756), and calibration performance. The relatively small sample size and reliance on structured clinical predictors may have contributed to the superior performance of LR in this study. In conclusion, LR demonstrated the most reliable predictive performance for this dataset.  Future research should use larger, diverse datasets and incorporate a broader range of predictors, including imaging and genomic data, to optimize the benefits of more complex machine learning models.
COMPARING LOGISTIC REGRESSION AND MACHINE LEARNING MODELS FOR CHRONIC KIDNEY DISEASE PREDICTION USING INDONESIAN POPULATION SURVEY DATA Nandina Salsabila; Soenarnatalina Melaniani; Kurnia Dwi Artanti; Sigit Ari Saputro; Heru Wijono; Jasni Mohamad Zain
Jurnal Biometrika dan Kependudukan Vol. 15 No. 2 (2026): JURNAL BIOMETRIKA DAN KEPENDUDUKAN : IN PRESS
Publisher : Universitas Airlangga

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Abstract

Early detection is important since chronic kidney disease (CKD) is a major public health problem. This study aimed to compare the discrimination, calibration, and classification performance of logistic regression and four machine learning algorithms for predicting CKD using nationally representative survey data from Indonesia. A Cross-sectional study was conducted using data from the 2023 Indonesian Health Survey (n=15,249). The data were split into training and test sets at an 80:20 ratio to develop five models, namely Logistic Regression, Decision Tree, Random Forest, XGBoost, and Artificial Neural Network. Model performance was assessed using AUC, calibration tests, and classification criteria, with survey weights were incorporated into all performance estimates. The discrimination ability of the machine learning algorithms was higher in the training set (AUC of up to 0.867); however, this advantage decreased in the test set (AUCs of 0.756 – 0.801). In terms of performance consistency and the highest test AUC (0.801), logistic regression was found to be more reliable. Logistic regression also exhibited better calibration, whereas the machine learning models showed evidence of overfitting and miscalibration. Overall, machine learning models did not demonstrate superior predictive performance compared with logistic regression for CKD prediction in this population-based study.
Co-Authors Heru Wijono Jasni Mohamad Zain Kurnia Dwi Artanti Nabila Shafiya Nandina Salsabila Ronny Isnuwardana Sagar Tiwari Soenarnatalina Melaniani