Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer’s Drug Discovery with LightGBM

Noviandy, Teuku Rizky; Nisa, Khairun; Idroes, Ghalieb Mutig; Hardi, Irsan; Sasmita, Novi Reandy

doi:10.62411/jcta.10129

Cited by 9 publications

(2 citation statements)

References 25 publications

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“…This strategy ensures that missing values are replaced with representative statistics, maintaining the integrity of the dataset. Subsequently, we apply label encoding to prepare categorical features for ML algorithms, assigning unique numerical labels to each category [27]. This transformation facilitates the processing of categorical data, enabling algorithms to effectively interpret and utilize these features during model training.…”

Section: Data Preprocessingmentioning

confidence: 99%

Interpretable Machine Learning for Chronic Kidney Disease Diagnosis: A Gaussian Processes Approach

Noviandy,

Idroes,

Syukri

et al. 2024

Indo. Jour. of Case Rep.

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Chronic Kidney Disease (CKD) is a global health issue impacting over 800 million people, characterized by a gradual loss of kidney function leading to severe complications. Traditional diagnostic methods, relying on laboratory tests and clinical assessments, have limitations in sensitivity and are prone to human error, particularly in the early stages of CKD. Recent advances in machine learning (ML) offer promising tools for disease diagnosis, but a lack of interpretability often hinders their adoption in clinical practice. Gaussian Processes (GP) provide a flexible ML model capable of delivering predictions and uncertainty estimates, essential for high-stakes medical applications. However, the integration of GP with interpretable methods remains underexplored. We developed an interpretable CKD classification model to address this knowledge gap by combining GP with Shapley Additive Explanations (SHAP). We assessed the model's performance using three GP kernels (Radial Basis Function, Matern, and Rational Quadratic). The results show that the Rational Quadratic kernel outperforms the other kernels, achieving an accuracy of 98.75%, precision of 100%, sensitivity of 97.87%, specificity of 100%, and an F1-score of 98.51%. SHAP values indicate that haemoglobin and specific gravity are the most influential features. The results demonstrate that the Rational Quadratic kernel enhances predictive accuracy and provides robust uncertainty estimates and interpretable explanations. This combination of accuracy and interpretability supports clinicians in making informed decisions and improving patient management and outcomes in CKD. Our study connects advanced ML techniques with practical medical applications, leading to more effective and reliable ML-driven healthcare solutions.

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Section: Data Preprocessingmentioning

confidence: 99%

Interpretable Machine Learning for Chronic Kidney Disease Diagnosis: A Gaussian Processes Approach

Noviandy,

Idroes,

Syukri

et al. 2024

Indo. Jour. of Case Rep.

Self Cite

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“…In recent years, machine learning has emerged as a powerful tool for predictive analytics, offering advanced techniques to analyze large datasets and uncover hidden patterns [9][10][11]. With their ability to process complex data and generate accurate predictions, machine learning models have been widely adopted across various domains, including customer churn prediction [12].…”

Section: Introductionmentioning

confidence: 99%

A Model-Agnostic Interpretability Approach to Predicting Customer Churn in the Telecommunications Industry

Noviandy,

Idroes,

Hardi

et al. 2024

Infolitika J. Data Sci.

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Customer churn is critical for businesses across various industries, especially in the telecommunications sector, where high churn rates can significantly impact revenue and growth. Understanding the factors leading to customer churn is essential for developing effective retention strategies. Despite the predictive power of machine learning models, there is a growing demand for model interpretability to ensure trust and transparency in decision-making processes. This study addresses this gap by applying advanced machine learning models, specifically Naïve Bayes, Random Forest, AdaBoost, XGBoost, and LightGBM, to predict customer churn in a telecommunications dataset. We enhanced model interpretability using SHapley Additive exPlanations (SHAP), which provides insights into feature contributions to predictions. Here, we show that LightGBM achieved the highest performance among the models, with an accuracy of 80.70%, precision of 84.35%, recall of 90.54%, and an F1-score of 87.34%. SHAP analysis revealed that features such as tenure, contract type, and monthly charges are significant predictors of customer churn. These results indicate that combining predictive analytics with interpretability methods can provide telecom companies with actionable insights to tailor retention strategies effectively. The study highlights the importance of understanding customer behavior through transparent and accurate models, paving the way for improved customer satisfaction and loyalty. Future research should focus on validating these findings with real-world data, exploring more sophisticated models, and incorporating temporal dynamics to enhance churn prediction models' predictive power and applicability.

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Explainable Deep Learning Approach for Mpox Skin Lesion Detection with Grad-CAM

Idroes,

Noviandy,

Emran

et al. 2024

Heca J. Appl. Sci.

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Mpox is a viral zoonotic disease that presents with skin lesions similar to other conditions like chickenpox, measles, and hand-foot-mouth disease, making accurate diagnosis challenging. Early and precise detection of mpox is critical for effective treatment and outbreak control, particularly in resource-limited settings where traditional diagnostic methods are often unavailable. While deep learning models have been applied successfully in medical imaging, their use in mpox detection remains underexplored. To address this gap, we developed a deep learning-based approach using the ResNet50v2 model to classify mpox lesions alongside five other skin conditions. We also incorporated Grad-CAM (Gradient-weighted Class Activation Mapping) to enhance model interpretability. The results show that the ResNet50v2 model achieved an accuracy of 99.33%, precision of 99.34%, sensitivity of 99.33%, and an F1-score of 99.32% on a dataset of 1,594 images. Grad-CAM visualizations confirmed that the model focused on relevant lesion areas for its predictions. While the model performed exceptionally well overall, it struggled with misclassifications between visually similar diseases, such as chickenpox and mpox. These results demonstrate that AI-based diagnostic tools can provide reliable, interpretable support for clinicians, particularly in settings with limited access to specialized diagnostics. However, future work should focus on expanding datasets and improving the model's capacity to distinguish between similar conditions.

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Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer’s Drug Discovery with LightGBM

Cited by 9 publications

References 25 publications

Interpretable Machine Learning for Chronic Kidney Disease Diagnosis: A Gaussian Processes Approach

Interpretable Machine Learning for Chronic Kidney Disease Diagnosis: A Gaussian Processes Approach

A Model-Agnostic Interpretability Approach to Predicting Customer Churn in the Telecommunications Industry

Explainable Deep Learning Approach for Mpox Skin Lesion Detection with Grad-CAM

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