hyOPTGB: An Efficient OPTUNA Hyperparameter Optimization Framework for Hepatitis C Virus (HCV) Disease Prediction in Egypt

Elshewey, Ahmed M.

doi:10.21203/rs.3.rs-2768795/v1

Cited by 2 publications

(2 citation statements)

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“…This approach is useful for summarizing the correlational matrix in a data set, where each element represents the coefficient of correlation between two variables. In the context of machine learning, heatmaps serve the purpose of revealing highly correlated variables within datasets, both in relation to target variables and among themselves [20].…”

Section: Figure 2 Correlation Of Stroke Datasetmentioning

confidence: 99%

Optimizing Stroke Mortality Prediction: A Comprehensive Study on Risk Factors Analysis and Hyperparameter Tuning Techniques

Tahyudin,

Tikaningsih,

Lestari

et al. 2024

TEM Journal

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Stroke is one of the major killer diseases in the world. Understanding the factors that influence the death of stroke patients is vital to improving patient care and outcomes. In this study, we used stroke patient data and machine learning techniques using a variety of algorithms, including Extreme Gradient Boosting, CatBoost, Extra Tree, Decision Tree, and Random Forest, to predict patient death after stroke. After performing hyperparameter settings, the XGBoost model achieved an accuracy of 86% with an AUC of 87. Significant improvements in the accuracy and predictive capability of this model after hyperparameter settings indicate a strong potential for clinical applications. In addition, our findings suggest that factors such as the patient's age, type of stroke, and blood pressure at the time of hospitalization have a significant impact on stroke patients' deaths. By understanding these factors, healthcare providers can improve patient intervention and management to reduce the risk of death after stroke. This research has made an important contribution to the development of a system for predicting the risk of death of stroke patients, which can help doctors and nurses identify high-risk patients and provide appropriate treatment.

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Section: Figure 2 Correlation Of Stroke Datasetmentioning

confidence: 99%

Optimizing Stroke Mortality Prediction: A Comprehensive Study on Risk Factors Analysis and Hyperparameter Tuning Techniques

Tahyudin,

Tikaningsih,

Lestari

et al. 2024

TEM Journal

View full text Add to dashboard Cite

show abstract

“…Optuna offers visualization tools to analyze the optimization process, providing insights into hyperparameter impacts and relationships. It has been effectively used in tuning deep neural networks [76], gradient boost models [80], AutoML systems [81], and reinforcement learning [82], among others, significantly improving model accuracy and performance [76,83].…”

Section: Hyperparameter Tunningmentioning

confidence: 99%

The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities

Trindade Neves,

Aparicio,

de Castro Neto

2024

Applied Sciences

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In the rapidly evolving landscape of urban development, where smart cities increasingly rely on artificial intelligence (AI) solutions to address complex challenges, using AI to accurately predict real estate prices becomes a multifaceted and crucial task integral to urban planning and economic development. This paper delves into this endeavor, highlighting the transformative impact of specifically chosen contextual open data and recent advances in eXplainable AI (XAI) to improve the accuracy and transparency of real estate price predictions within smart cities. Focusing on Lisbon’s dynamic housing market from 2018 to 2021, we integrate diverse open data sources into an eXtreme Gradient Boosting (XGBoost) machine learning model optimized with the Optuna hyperparameter framework to enhance its predictive precision. Our initial model achieved a Mean Absolute Error (MAE) of EUR 51,733.88, which was significantly reduced by 8.24% upon incorporating open data features. This substantial improvement underscores open data’s potential to boost real estate price predictions. Additionally, we employed SHapley Additive exPlanations (SHAP) to address the transparency of our model. This approach clarifies the influence of each predictor on price estimates and fosters enhanced accountability and trust in AI-driven real estate analytics. The findings of this study emphasize the role of XAI and the value of open data in enhancing the transparency and efficacy of AI-driven urban development, explicitly demonstrating how they contribute to more accurate and insightful real estate analytics, thereby informing and improving policy decisions for the sustainable development of smart cities.

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hyOPTGB: An Efficient OPTUNA Hyperparameter Optimization Framework for Hepatitis C Virus (HCV) Disease Prediction in Egypt

Cited by 2 publications

References 50 publications

Optimizing Stroke Mortality Prediction: A Comprehensive Study on Risk Factors Analysis and Hyperparameter Tuning Techniques

Optimizing Stroke Mortality Prediction: A Comprehensive Study on Risk Factors Analysis and Hyperparameter Tuning Techniques

The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities

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