=Paper=
{{Paper
|id=Vol-3896/paper15
|storemode=property
|title=Comparison of artificial intelligence algorithms and expert approach in risk classification in nephrology
|pdfUrl=https://ceur-ws.org/Vol-3896/paper15.pdf
|volume=Vol-3896
|authors=Dawid Pawuś,Szczepan Paszkiel,Tomasz Porażko
|dblpUrl=https://dblp.org/rec/conf/ittap/PawusPP24
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==Comparison of artificial intelligence algorithms and expert approach in risk classification in nephrology==
https://ceur-ws.org/Vol-3896/paper15.pdf
Comparison of artificial intelligence
algorithms and expert approach in risk
classification in nephrology
Dawid Pawuś1,*, Szczepan Paszkiel1 and Tomasz Porażko2,3
1
University of Technology, Prószkowska 76 Street, 45-758 Opole, Faculty of Electrical Engineering, Automatic Control
and Informatics, Poland
2
Departament of Internal Medicine and Nephrology, Institute of Medical Sciences, University of Opole, Oleska 48 Street,
Opole, 45–052, Poland
3
Departament of Nephrology with Dialysis Unit, Opole University Hospital, Witosa 26 Street, Opole, 46-020, Poland
Abstract
This study presents a comparative analysis of various artificial intelligence (AI) and machine learning
(ML) algorithms for risk classification in idiopathic membranous nephropathy (IMN), a complex
kidney disease. The research evaluates seven different models, including K-Nearest Neighbors,
Decision Trees, Random Forests, Support Vector Machines, Adaptive Boosting, LightGBM, and Multi-
layer Perceptron. The results reveal that ensemble methods, particularly Random Forests, achieve the
highest precision in classifying IMN risk levels, highlighting their potential in improving diagnostic
accuracy and patient management in nephrology. The study underscores the importance of model
selection and fine-tuning to optimize AI applications in clinical settings, providing a basis for future
advancements in AI-driven nephrology.
Keywords
Artificial Intelligence, Machine Learning, Expert System, Classification, Kidney, Nephrology,
Automation 1
1.Introduction
Nephrology is a critical area within medical sciences, with kidney diseases impacting
approximately 850 million people worldwide, as indicated by recent registries [1]. Like other
scientific disciplines, nephrology is currently experiencing a transformation driven by advanced
technologies. A key component of this evolution is the close collaboration between IT
engineering and medical professionals. This partnership is leading to an increase in research
efforts and a growing interest in developing and implementing AI-driven systems, numerical
and classification algorithms, as well as expert systems. These innovations have the potential to
enhance, automate, and refine the processes of diagnosing, classifying, and ultimately
improving the treatment outcomes for kidney diseases.
ITTAP’2024: 4th International Workshop on Information Technologies: Theoretical and Applied Problems,
November 20–22, 2024, Ternopil, Ukraine, Opole, Poland ∗ Corresponding author.
†
These authors contributed equally. dawid.pawus@student.po.edu.pl (D. Pawuś);
s.paszkiel@po.edu.pl (S. Paszkiel);
tomasz.porazko@usk.opole.pl (T. Porażko)
0000-0003-3308-3474 (D. Pawuś); 0000-0002-4917-5712 (S. Paszkiel); 0000-0002-8516-2740 (T.
Porażko)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Historically, nephrology has relied on manual analysis, which presents challenges when
dealing with complex data sets, often leading to diagnostic errors and suboptimal treatment
choices. The ongoing advancements in numerical algorithms and artificial intelligence offer new
opportunities to enhance kidney health monitoring. This progress allows for greater diagnostic
accuracy and more precise treatment strategies. The synergy between expert medical
knowledge and the automation capabilities of algorithms is paving the way for systems that can
effectively address complex nephrological challenges.
Glomerular diseases, a subset of kidney conditions, specifically target the glomeruli— tiny
structures in the kidneys that filter blood and produce urine. These diseases can arise from
various causes, including infections, autoimmune disorders, hypertension, diabetes, and other
metabolic conditions.
Figure 1: General outline of the procedure for research and verification of the problem.
This article provides a comparative approach to different AI algorithms that can be used in
risk classification of patients with a nephrological disease called idiopathic membranous
nephropathy (IMN). The concept is to create an extended and multi-task expert system that
would be tasked with supporting nephrologists’ decisions in their daily work in treating patients
with kidney diseases. The risk classification module is therefore crucial from the perspective of
the domain expert, who can determine the further course of action based on the patient’s
assignment to a specific group. The range of algorithms ranges from simple linear approaches to
complex and advanced gradient solutions. The expertise of nephrologists is also leveraged along
with advances in engineering. Focusing on specific applications, our goal is to show how these
advances can impact the standards of patient care, ultimately improving the outcomes of
traditional approaches. Figure 1 provides a general outline of the methodology, scope of the
study and the approach used to address the problem.
The next section will provide a detailed literature review and an overview of the state of the
art.
2.State of the art and related works
This literature review examines the use of automation, artificial intelligence, and classification
systems in the field of nephrology. It delves into how these advanced technologies streamline
processes, increase diagnostic precision, and improve patient outcomes. Through a critical
analysis of the current literature, the goal is to uncover trends and identify gaps, thereby guiding
future research toward innovative solutions in the field of nephrology.
Advancements in artificial intelligence, innovation, and transformative technologies are
pivotal in nephrology and dialysis [2–4]. Additionally, the future prospects of AI and modern
technologies in managing kidney diseases are extensively reviewed [5, 6]. Research focusing on
numerical models and machine learning techniques also holds considerable importance [7–9].
A key area in nephrology research is the integration of acute kidney injury (AKI) with AI-
driven predictive algorithms [10, 11]. Another significant focus includes the development of
predictive models in the context of personalized medicine [12]. AI-based clinical applications in
nephrology are explored in works by [13] and [14]. Furthermore, ethical considerations
surrounding AI applications are discussed in [15] and [16].
Recent research underscores the integration of AI and machine learning within nephrology.
For instance, [17] develops short-term prognosis prediction models for severe AKI patients
undergoing PIRRT (Prolonged Intermittent Renal Replacement Therapy), utilizing algorithms
such as Naive Bayes and Random Forest, and highlighting the critical role of serum electrolyte
management. Another study [18], introduces Sugeno's fuzzy inference system for regression
problems involving numerous variables and limited data, demonstrating superior performance
over traditional methods.
In addition, [19] presents health-disease phase diagrams (HDPDs) for visualizing disease
onset probabilities, while [20] develops CKD.Net, a hybrid model that achieves high accuracy in
predicting CKD stages. Other significant contributions include [21], which focuses on precise
kidney volume measurement using AI, and [22], which addresses PRCC (Papillary Renal Cell
Carcinoma) stage classification. Furthermore, [23] showcases the importance of machine
learning in predicting AKI and diagnosing PDAC (Pancreatic Ductal Adenocarcinoma).
A study derived and validated an ML risk score for predicting diabetic kidney disease
(DKD) progression using biomarker and electronic patient data [24]. Research into interpretable
ML for early AKI prognosis emphasizes model interpretability benefits [25]. ML has also been
utilized to predict primary nephrotic syndrome pathology without biopsy and identify hub
genes in membranous nephropathy [26, 27]. A model for predicting idiopathic membranous
nephropathy prognosis is discussed in [28]. ML models predicting rituximab underdosing in
membranous nephropathy show high accuracy, sensitivity, and specificity [29]. Another study
reports a machine learning framework for diagnosing membranous nephropathy from whole-
slide images, achieving a 97.32% F1-score [30].
A belief rule-based system for diagnosing primary membranous nephropathy shows
significant reliability with 98% sensitivity, 96.9% specificity, 97.8% accuracy, and an AUC of 0.93
[31]. A predictive model for long-term renal function impairment post-minimally invasive
partial nephrectomy has a concordance index of 0.75 [32]. The use of AI and ML in dialysis is
reviewed, focusing on diagnostics, prognosis, and treatment optimization [33]. Guidelines for
proper application and transparent reporting of ML models in biomedical research emphasize
best practices [34]. Recent literature highlights the crucial role of clinical prediction models in
modern healthcare, emphasizing challenges and the need for transparent reporting to assess
their quality [35]. The literature underscores the importance of understanding diagnostic and
prognostic prediction models, addressing issues such as model development, validation, and
sample size considerations to improve clinical decision-making [36–38], as well as valuable
research [39, 40].
3. Structure, research scope and methodology in the
problem of automatic risk classification
This section explores the development of an expert system for diagnosing and treating
idiopathic membranous nephropathy, focusing specifically on the risk classification module.
The system is structured as a hierarchical decision tree that integrates clinical data, diagnostic
criteria, and therapeutic guidelines to assist nephrologists in managing IMN.
3.1. General overview of system modules
The expert system consists of several key components, which are shown in Figure 2 and briefly
described below:
1. Diagnostic Module – the diagnosis of (IMN) is confirmed through a combination of
patient history, diagnostic tests, and kidney biopsy. The evaluation also considers the
likelihood of progression to stage 5 chronic kidney disease (CKD5) by analyzing
glomerular filtration rate, levels of proteinuria, and serum albumin concentrations.
2. Risk Stratification – patients are classified into four distinct risk categories – low,
intermediate, high, and very high – based on factors such as estimated glomerular
filtration rate (eGFR) and proteinuria levels. Each category is associated with specific
management strategies tailored to the risk profile.
3. Therapeutic Module – treatment recommendations vary from conservative
management to more aggressive immunosuppressive therapies, depending on the risk
category. Options include ACE inhibitors or ARBs, rituximab, calcineurin inhibitors,
cyclophosphamide, and corticosteroids.
4. Treatment Effectiveness Assessment Module and Treatment Continuation Module –
patient response to treatment is re-evaluated after six months. Based on clinical
indicators, the treatment plan may be continued, modified, or altered. Protocols are
provided for deciding on the appropriate course of action.
5. Follow-up Module – guidelines are provided for the long-term monitoring and
management of patients who achieve partial or complete remission, including strategies
for addressing treatment resistance and managing relapses.
6. User Interface – the system includes a user-friendly graphical interface designed for ease
of use by healthcare professionals.
Figure 2: General diagram of the system with the module being the object of the study marked.
In this article, we focus solely on the Risk Stratification Module (see Fig. 2) of the expert
system, which plays a crucial role in assigning patients to specific risk groups for targeted
management. The comparative analysis involves various AI algorithms, from basic linear
methods to advanced gradient-based solutions, alongside traditional expert approaches.
The details will be explained in the following sections of the article.
The remaining components of the system – such as diagnostic, therapeutic, and followup
modules – will be explored in future research. This initial focus on risk classification aims to
demonstrate how AI advancements can enhance traditional nephrological practices and
improve patient outcomes.
3.2. Input-output model of the classification system
This subsection details the process of modeling the classification system, presenting the input
variables and their corresponding output classifications. The classification model processes
clinical parameters to categorize IMN patients into risk groups using a Multi Input Single Output
(MISO) framework. The authors used the information provided in the guidelines in [41]. The 11
inputs to this model, detailed below, include a range of variables:
1. eGFR (Estimated Glomerular Filtration Rate) – a measure of kidney function estimating
how well the kidneys filter blood.
2. Proteinuria – the level of protein present in the urine.
3. Serum Albumin Concentration – the concentration of albumin in the blood.
4. Response to ACEi/ARB Treatment – the percentage reduction in proteinuria after 6
months of treatment with ACE inhibitors or ARBs.
5. Serum anti-PLA2R Concentration – the level of anti-PLA2R antibodies in the serum.
6. Urinary α1-microglobulin Concentration – the concentration of α1-microglobulin in the
urine.
7. Urinary IgG Concentration – the concentration of IgG in the urine.
8. Urinary β2-microglobulin Concentration – the concentration of β2-microglobulin in the
urine.
9. Selectivity Index – the ratio of different urinary protein components indicating the
selectivity of proteinuria.
10. Nephrotic Syndrome Symptoms – the presence of severe symptoms associated with
nephrotic syndrome (binary value).
11. Rapid Renal Function Impairment – a swift decline in kidney function not attributable to
other diseases (binary value).
The classification model uses machine learning to provide accurate and reliable risk
assessments. Risk categories are defined by specific clinical criteria, and integrating machine
learning techniques significantly increases the model’s capabilities:
1. Low Risk
• eGFR > 60 ml/min/1.73 m²
• Proteinuria < 3.5 g/d
• Serum albumin > 30 g/l OR
• eGFR > 60 ml/min/1.73 m²
• Proteinuria < 3.5 g/d or a reduction > 50% after 6 months of ACEi/ARB treatment
2. Moderate Risk
• eGFR > 60 ml/min/1.73 m²
• Proteinuria > 3.5 g/d and no reduction > 50% after 6 months of ACEi/ARB treatment OR
• Does not meet high risk criteria
3. High Risk
• eGFR < 60 ml/min/1.73 m² and/or Proteinuria > 8 g/d for 6 months OR
• eGFR > 60 ml/min/1.73 m²
• Proteinuria > 3.5 g/d and no reduction > 50% after 6 months of ACEi/ARB treatment, plus
one of the following:
a. Serum albumin < 2.5 g/dl
b. Serum anti-PLA2R > 50 RU/ml
c. Urinary β2-microglobulin > 40 μg/min
d. Urinary IgG > 1 μg/min
e. Urinary β2-microglobulin > 250 mg/d
f. Selectivity Index > 0.20
g. Very High Risk
h. Life-threatening nephrotic syndrome symptoms OR
• Rapidly progressive renal impairment not caused by other diseases.
Machine learning algorithms are excellent at identifying complex interactions between
multiple input variables that may not be apparent with rule-based logic alone. This leads to a
more nuanced understanding of a patient’s risk profile. The guidelines above provide a
framework for categorizing data, but they cannot account for every possible combination of lab
results and other factors. Nor could a rule-based system easily provide a way to determine which
output category values fall into. This gives AI and ML methods an advantage in these types of
tasks.
Machine learning models leverage historical patient data to identify patterns and correlations
that enhance the precision of risk classification predictions. This approach, driven by data,
refines decision boundaries and reduces the likelihood of misclassification. A key benefit of
machine learning models is their capacity to adapt and become more accurate as new data is
introduced. Furthermore, certain machine learning models offer insights into the probability of
data belonging to specific classes.
The next section will describe in detail the considered algorithms.
4.Overview of algorithms used in the study
In any problem where automatic classification of collected data is needed, selecting the right AI
models and numerical algorithms is a key step in developing a robust classification system.
Seven AI and ML-based models were selected and tested to provide the best solution to the task.
The models feature different algorithms used, such as tree-based methods, ensemble techniques,
support vector machines, and neural networks. By using different methods, a comprehensive
evaluation and comparison is provided. It also provides researchers with an answer as to which
model performs best in classifying the risk of IMN patients. Below is a brief description of each
algorithm:
1. K-Nearest Neighbors (KNN) is a straightforward, non-parametric method used for
both classification and regression. It classifies a data point by considering the most
common class among its 𝑘 nearest neighbors in the feature space. While KNN is simple
to grasp and implement, it can become computationally heavy with larger datasets.
2. Decision trees (DT) offer a flexible approach to machine learning for both classification
and regression tasks. They partition data into subsets based on feature values, creating a
tree-like structure of decisions. Though easy to interpret, decision trees can overfit the
data if not properly managed through pruning.
3. Random forests (RF) are an ensemble learning method that builds and combines
multiple decision trees to achieve more accurate and stable predictions. By using
different subsets of data and features to construct each tree, random forests enhance
generalization and help reduce overfitting.
4. Support Vector Machines (SVMs) are effective classifiers that determine the optimal
hyperplane for separating classes in the feature space. They work well in high-
dimensional settings and can address both linear and non-linear classification problems
through kernel functions.
5. Adaptive Boosting (AdaBoost) is an ensemble technique that merges several weak
classifiers into a strong one. It adjusts the weights of misclassified instances during each
iteration, placing greater emphasis on harder-to-classify examples. Despite its
simplicity, AdaBoost can be sensitive to noisy data.
6. LightGBM is a gradient boosting framework designed for efficiency and scalability,
particularly with large datasets. It uses tree-based learning algorithms and includes
optimizations to improve both speed and memory efficiency.
7. The Multi-layer Perceptron (MLP) Classifier is a type of neural network with multiple
layers of neurons. It is adept at capturing complex patterns in data and can be used for
classification and regression. However, MLPs require careful tuning of hyperparameters
and can involve lengthy training processes.
This chapter briefly introduces seven different AI and ML models for their usefulness in
classifying the risk of IMN patients. The models reviewed include K-Nearest Neighbors,
Decision Trees, Random Forests, Support Vector Machines, Adaptive Boosting, LightGBM, and
the Multi-layer Perceptron Classifier. Each model offers unique strengths: KNN is
straightforward but computationally intense, DTs are interpretable but prone to overfitting. RFs
improve stability and generalization, SVMs excel in high-dimensional spaces and AdaBoost
focuses on hard-to-classify examples but can be sensitive to noise. LightGBM provides efficiency
and scalability for large datasets, and MLPs capture complex patterns but require extensive
tuning.
The next chapter will detail the training process of these models and provide a discussion of
the results obtained from their application to the classification task.
5.Training of models and discussion of achieved results
In this section, we present the insights gained from the analysis and the results of classifying
nephrology patient data using various machine learning models described in Section 4.
The original dataset included laboratory results relevant to IMN risk classification. To
mitigate class imbalance and improve model performance, synthetic data augmentation was
applied, generating new data points that mirror the distribution and features of the existing data.
For model training, 200 datasets were used, evenly distributed among the four risk categories:
low, medium, high, and very high, with 50 datasets in each category. An identical number of
datasets were allocated for testing. This approach ensured that the models had ample data for
training and a reliable set for evaluation.
The comparison of precision scores (see Fig. 3) across various machine learning models in the
context of nephrology risk classification reveals important insights into their performance on
the dataset prepared and described in the preceding sections.
Figure 3: Precision results for tested models.
By analyzing the results in Figure 3, we can discuss in detail the results and implications of
each classifier:
1. MLP Classifier – the MLP classifier achieved a high precision score of 0.96, indicating its
robustness in accurately predicting the positive class in the IMN risk classification task.
This model's architecture, which simulates the neural networks found in biological
brains, has proven effective in capturing the complex, non-linear relationships inherent
in the clinical dataset.
2. LightGBM – in contrast, the LightGBM model exhibited a notably lower precision score
of 0.11. This result is surprising given LightGBM's reputation for efficiency and high
performance in many classification tasks. The poor performance may suggest that the
specific characteristics of the nephrology dataset, or the hyperparameters used, do not
align well with the strengths of this gradient boosting framework. Further investigation
into feature importance and model tuning would be necessary to understand this
anomaly.
3. AdaBoost – Adaptive Boosting yielded a precision score of 0.89, demonstrating its
capability to enhance weak classifiers by focusing on misclassified instances. This
performance indicates that AdaBoost effectively leveraged the synthetic data
augmentation and was able to generalize well across the test dataset.
4. Support Vector Machine – the SVM classifier, with a precision score of 0.93, performed
robustly, confirming its strength in high-dimensional spaces, where it constructs
optimal hyperplanes to segregate different risk classes. This suggests that the SVM is
particularly well-suited to handle the complexity of the feature space derived from
nephrology patient data.
5. Random Forest – the RF model achieved the highest precision score of 0.98. This
ensemble method's exceptional performance underscores its ability to manage the
variability within the dataset by combining the predictions of multiple decision trees,
thereby enhancing overall predictive accuracy.
6. Decision Tree – the single DT model also performed well, with a precision score of 0.96.
Despite its simplicity compared to ensemble methods, the decision tree's interpretability
and effectiveness in handling this particular dataset are evident from its high score.
7. K-Nearest Neighbors – KNN classifier attained a precision score of 0.88. While KNN is
often sensitive to the local structure of the data and can be affected by noise, its
performance in this scenario indicates a reasonable degree of success in classifying risk
levels among nephrology patients.
In summary, the Random Forest model emerged as the most effective classifier in terms of
precision, followed closely by the Decision Tree and MLP classifiers. The unusually low
precision score of LightGBM warrants further exploration to identify potential causes and
corrective measures. These findings will inform future work in refining model selection and
optimization for nephrology risk classification tasks.
Figure 4: F1-score results for tested models.
The F1-score evaluation of different classifiers (see Fig. 4), as presented in the nephrology risk
classification study, provides deeper insights into the balance between precision and recall
across various models. Here, we discuss the performance of each classifier in terms of its F1-
score, which is particularly useful in assessing the model's effectiveness in dealing with the
complexities of an imbalanced dataset:
1. MLP Classifier – F1-score of 0.96 confirms strong capability in managing complex, non-
linear classification, balancing precision and recall effectively
2. LightGBM – low F1-score of 0.16, suggesting significant struggles with both precision
and recall, making it less suitable for this task.
3. Adaptive Boosting – F1-score of 0.84 reflects good performance, particularly in
enhancing classification of difficult instances in imbalanced data.
4. Support Vector Machine – SVM scored 0.89 in F1, indicating strong performance with
well-balanced precision and recall, effectively separating risk classes.
5. Random Forest – excelled with an F1-score of 0.99, showcasing superior performance by
combining multiple decision trees to minimize errors.
6. Decision Tree – DT achieved a high F1-score of 0.94, demonstrating effective
classification by accurately partitioning the feature space.
7. K-Nearest Neighbors – F1-score of 0.79 indicates moderate performance, reflecting
challenges in balancing precision and recall, particularly with imbalanced data.
In conclusion, the Random Forest classifier once again proved to be the most effective model
in terms of the F1-score, closely followed by the MLP and Decision Tree classifiers. The
consistently low performance of LightGBM, as reflected in both precision and F1-score, warrants
further exploration. Overall, these results provide clear guidance on the most appropriate
machine learning models for nephrology risk classification tasks, with a particular emphasis on
the effectiveness of ensemble methods and neural network-based approaches.
Figure 5: Recall results for tested models.
The recall scores (see Fig. 5) across various classifiers in the context of nephrology risk
classification provide key insights into each model's ability to identify relevant cases within the
dataset. Below is a summary of the performance for each classifier:
1. MLP Classifier – the MLP achieved a high recall score of 0.96, confirming its strong
ability to identify nearly all relevant cases, making it one of the most effective models in
this study.
2. LightGBM – the LightGBM model exhibited a low recall score of 0.25, indicating
significant issues with sensitivity and a tendency to miss a substantial number of
relevant cases.
3. Adaptive Boosting – AdaBoost scored 0.85 in recall, suggesting good sensitivity, as it
effectively identified most of the relevant instances in the dataset.
4. Support Vector Machine – the SVM achieved a recall score of 0.88, showing strong
sensitivity and a solid ability to correctly identify relevant cases, with few false
negatives.
5. Random Forest – with an exceptional recall score of 0.99, the Random Forest model
nearly perfectly identified relevant instances, making it highly reliable in this
classification task.
6. Decision Tree – the DT model recorded a high recall score of 0.93, demonstrating its
effectiveness in capturing most relevant cases and minimizing false negatives.
7. K-Nearest Neighbors – KNN classifier achieved a recall score of 0.81, indicating decent
sensitivity but missing some relevant instances, reflecting moderate recall performance.
In summary, the Random Forest model again demonstrated the highest reliability with its
near-perfect recall score, followed closely by the MLP and Decision Tree classifiers. The low
recall score of LightGBM, similar to its performance in other metrics, suggests that it struggled
significantly with this specific task.
Figure 6: Example results of the confusion matrix for models.
The confusion matrices (see Fig. 6) presented in the provided figure summarize the
performance of four different machine learning algorithms – KNN, LightGBM, RF, and DT.
The results presented in Figure 6 clearly indicate that the RF algorithm in the classification
model achieved the best results in the course of the study. On the other hand, LightGBM,
similarly to the graphics presented in Figures 3-5, performed the worst, which is confirmed by
the result on the confusion matrix. In the case of the other two algorithms – KNN and DT, their
efficiency in the task of risk classification of patients with IMN is confirmed to be decent, but not
as good as for RF.
6.Conclusions
The comparative analysis of various artificial intelligence and machine learning algorithms for
the classification of idiopathic membranous nephropathy risk demonstrates the potential of
these technologies in enhancing traditional nephrological practices. The study evaluated seven
different AI models, including K-Nearest Neighbors, Decision Trees, Random Forests, Support
Vector Machines, Adaptive Boosting, LightGBM, and Multi-layer Perceptron Classifier.
The results indicate that ensemble methods such as Random Forests outperform other
models, achieving a precision score of 0.98, demonstrating their robustness in handling the
variability within nephrological data. The MLP Classifier and Decision Tree also showed high
precision scores (0.96), suggesting their capability in capturing complex relationships within the
clinical dataset. In contrast, LightGBM's performance was unexpectedly low, with a precision
score of 0.11, indicating that further tuning and investigation into model parameters are
required for this specific application.
The study highlights that AI models, particularly ensemble methods, can significantly
improve the accuracy of risk classification in nephrology, potentially leading to better patient
outcomes. However, the results also underscore the importance of selecting appropriate models
and fine-tuning them to the specific characteristics of medical datasets. Future research should
focus on optimizing these models and exploring their integration into clinical decision support
systems to assist nephrologists in managing kidney diseases more effectively.
Overall, this work provides a foundation for the continued exploration and application of AI
in nephrology, emphasizing the need for further validation and refinement to achieve reliable
and clinically applicable tools.
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