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JSDP 2025, 22(2): 79-96 |
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Azarnavid B, Abdolhosseinzadeh M, Emami H. Stacking machine learning model for classification and prediction of liver diseases. JSDP 2025; 22 (2) : 5
URL: http://jsdp.rcisp.ac.ir/article-1-1454-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1454-en.html
University of Bonab
Abstract: (37 Views)
Liver diseases are among the leading causes of mortality worldwide, deeply influencing individuals' lives, often at younger ages when they are in the prime of their personal and professional lives. The insidious nature of these diseases lies in their early signs initial symptoms, which frequently go unnoticed until the condition has progressed to an advanced stage. This delay in diagnosis not only diminishes the chances of successful treatment but also places an immense emotional and financial burden on patients and their families. Early detection is therefore critical, as it can significantly alter the course of the disease, improving survival rates and quality of life. However, traditional diagnostic methods often fall short in terms of speed, accuracy, and accessibility, particularly in resource-limited settings. This underscores the urgent need for innovative approaches to liver disease detection and management.
Machine learning (ML) has emerged as a powerful tool in this regard, offering the potential to revolutionize how we diagnose and predict liver diseases. By leveraging vast datasets—ranging from clinical records and laboratory results to imaging data—ML algorithms can uncover complex patterns and correlations that may elude human experts. These insights can lead to earlier and more accurate diagnoses, enabling timely interventions that can save lives. Among the various ML approaches, stacked machine learning (SML) models stand out for their ability to combine the strengths of multiple algorithms, mitigating the limitations of individual models and enhancing overall performance. This research focuses on developing and evaluating an SML model specifically designed for the accurate diagnosis, classification, and prediction of liver diseases, with the goal of addressing some of the most pressing challenges in this field.
The proposed SML model employs a sophisticated two-layer architecture to tackle common issues such as overfitting and to improve prediction accuracy. In the first layer, the model integrates four robust base learner algorithms: Extremely Randomized Trees (ET), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB). Each of these algorithms contributes unique strengths, such as handling high-dimensional data, capturing non-linear relationships, and reducing variance. The predictions generated by these base learners are then fed into the second layer, where a Logistic Regression (LR) algorithm synthesizes the outputs to produce the final prediction. This layered approach ensures that the model benefits from the collective intelligence of multiple algorithms, resulting in more reliable and precise outcomes. To further optimize performance, the Grid Search (GS) algorithm was employed to fine-tune the parameters of the learning algorithms, ensuring that the model operates at its full potential. This study employs dataset from the University of California, Irvine (UCI) Machine Learning Repository. A sample size of 615 instances was utilized to implement the proposed methodologies, with a stratified division of 70% for training and 30% allocated for testing purposes. The results of this research are highly promising. Evaluation based on 5-fold cross-validation demonstrates that the proposed SML model outperforms existing methods, achieving an impressive 0.9940 accuracy and a 0.9880 F1-score on the test data. These metrics not only highlight the model's exceptional predictive capabilities but also underscore its potential to serve as a valuable tool for clinicians in real-world settings. By providing accurate and timely diagnoses, the SML model can help reduce the mortality and morbidity associated with liver diseases, offering hope to patients and their families.
Beyond the technical achievements, the human impact of this research cannot be overstated. For patients, the SML model represents a lifeline—a chance to detect liver diseases early, when treatment is most effective, and to avoid the devastating consequences of late-stage diagnoses. For healthcare providers, it offers a reliable and efficient diagnostic tool that can enhance decision-making and improve patient outcomes. Also, for society as a whole, it signifies a step forward in the fight against a disease that disproportionately affects vulnerable populations, including those in underserved regions where access to advanced medical care is limited. In essence, this research is not just about developing a sophisticated algorithm; it is about harnessing the power of machine learning to make a tangible difference in people's lives. By bridging the gap between cutting-edge technology and human care, the proposed SML model embodies the potential of computer science to address some of the most critical health challenges of our time. It is a testament to the transformative power of innovation, compassion, and collaboration in the pursuit of better health for all.
Machine learning (ML) has emerged as a powerful tool in this regard, offering the potential to revolutionize how we diagnose and predict liver diseases. By leveraging vast datasets—ranging from clinical records and laboratory results to imaging data—ML algorithms can uncover complex patterns and correlations that may elude human experts. These insights can lead to earlier and more accurate diagnoses, enabling timely interventions that can save lives. Among the various ML approaches, stacked machine learning (SML) models stand out for their ability to combine the strengths of multiple algorithms, mitigating the limitations of individual models and enhancing overall performance. This research focuses on developing and evaluating an SML model specifically designed for the accurate diagnosis, classification, and prediction of liver diseases, with the goal of addressing some of the most pressing challenges in this field.
The proposed SML model employs a sophisticated two-layer architecture to tackle common issues such as overfitting and to improve prediction accuracy. In the first layer, the model integrates four robust base learner algorithms: Extremely Randomized Trees (ET), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB). Each of these algorithms contributes unique strengths, such as handling high-dimensional data, capturing non-linear relationships, and reducing variance. The predictions generated by these base learners are then fed into the second layer, where a Logistic Regression (LR) algorithm synthesizes the outputs to produce the final prediction. This layered approach ensures that the model benefits from the collective intelligence of multiple algorithms, resulting in more reliable and precise outcomes. To further optimize performance, the Grid Search (GS) algorithm was employed to fine-tune the parameters of the learning algorithms, ensuring that the model operates at its full potential. This study employs dataset from the University of California, Irvine (UCI) Machine Learning Repository. A sample size of 615 instances was utilized to implement the proposed methodologies, with a stratified division of 70% for training and 30% allocated for testing purposes. The results of this research are highly promising. Evaluation based on 5-fold cross-validation demonstrates that the proposed SML model outperforms existing methods, achieving an impressive 0.9940 accuracy and a 0.9880 F1-score on the test data. These metrics not only highlight the model's exceptional predictive capabilities but also underscore its potential to serve as a valuable tool for clinicians in real-world settings. By providing accurate and timely diagnoses, the SML model can help reduce the mortality and morbidity associated with liver diseases, offering hope to patients and their families.
Beyond the technical achievements, the human impact of this research cannot be overstated. For patients, the SML model represents a lifeline—a chance to detect liver diseases early, when treatment is most effective, and to avoid the devastating consequences of late-stage diagnoses. For healthcare providers, it offers a reliable and efficient diagnostic tool that can enhance decision-making and improve patient outcomes. Also, for society as a whole, it signifies a step forward in the fight against a disease that disproportionately affects vulnerable populations, including those in underserved regions where access to advanced medical care is limited. In essence, this research is not just about developing a sophisticated algorithm; it is about harnessing the power of machine learning to make a tangible difference in people's lives. By bridging the gap between cutting-edge technology and human care, the proposed SML model embodies the potential of computer science to address some of the most critical health challenges of our time. It is a testament to the transformative power of innovation, compassion, and collaboration in the pursuit of better health for all.
Article number: 5
Keywords: Liver diseases, early diagnosis, machine learning, cumulative machine learning model, cross-validation
Type of Study: Research |
Subject:
Paper
Received: 2025/01/26 | Accepted: 2025/07/21 | Published: 2025/09/13 | ePublished: 2025/09/13
Received: 2025/01/26 | Accepted: 2025/07/21 | Published: 2025/09/13 | ePublished: 2025/09/13
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