Advanced Anemia Classification Using Comprehensive Hematological Profiles and Explainable Machine Learning Approaches DOI Creative Commons
Teuku Rizky Noviandy,

Ghifari Maulana Idroes,

Rivansyah Suhendra

et al.

Infolitika Journal of Data Science, Journal Year: 2024, Volume and Issue: 2(2), P. 72 - 81

Published: Nov. 29, 2024

Anemia is a common health issue with serious clinical effects, making timely and accurate diagnosis essential to prevent complications. This study explores the use of machine learning (ML) methods classify anemia its subtypes using detailed hematological data. Six ML models were tested: Gradient Boosting, Random Forest, Naive Bayes, Logistic Regression, Support Vector Machine, K-Nearest Neighbors. The dataset was preprocessed feature standardization Synthetic Minority Oversampling Technique (SMOTE) address class imbalance. Boosting delivered highest accuracy, sensitivity, F1-score, establishing itself as top-performing model. SHapley Additive exPlanations (SHAP) analysis applied enhance model interpretability, identifying key predictive features. highlights potential explainable develop efficient, accurate, scalable tools for diagnosis, fostering improved healthcare outcomes globally.

Language: Английский

Unleashing the power of innovation and sustainability: Transforming cereal production in the BRICS countries DOI Creative Commons
Cosimo Magazzino,

Tulia Gattone,

Muhammad Usman

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 167, P. 112618 - 112618

Published: Sept. 16, 2024

Language: Английский

Citations

5
AI for climate change: unveiling pathways to sustainable development through greenhouse gas emission predictions DOI
Saïd Toumi,

Abdussalam Aljadani,

Hassen Toumi

et al.

Eurasian economic review, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 3, 2025

Language: Английский

Citations

0
On the application of multi-criteria decision-making methods in environmental pollution management: a comprehensive systematic review DOI
Soroush Safarzadeh, Hamed Jafari

Environment Development and Sustainability, Journal Year: 2025, Volume and Issue: unknown

Published: March 15, 2025

Language: Английский

Citations

0
Global determinants of methane emissions in OECD countries: A dynamic panel approach DOI Creative Commons
Jana Chovancová, Manuel A. Zambrano‐Monserrate, Brahim Bergougui

et al.

Research in Globalization, Journal Year: 2024, Volume and Issue: 9, P. 100232 - 100232

Published: June 9, 2024

Methane (CH4), an often-overlooked greenhouse gas (GHG), has a significant impact on the environment. Although it receives less attention than carbon dioxide (CO2), is second most important GHG in terms of its ability to trap heat atmosphere. Few studies have analyzed determinants CH4 emissions, especially those from energy sector. Therefore, this study provides relevant information GDP, primary and renewable consumption, human development index trade openness methane emissions OECD countries. Using advanced cointegration approaches, we find that GDP consumption increase while mitigate their growth. However, these variables varied over time. No effect was found. We recommend specific policies for countries reduce polluting Governments should promote sources (solar, wind, hydro) reliance fossil fuels, thereby minimizing leakage during extraction transport. In addition, investing can sustainable behaviors further addressing both environmental social concerns.

Language: Английский

Citations

3
A Comparative Analysis of Advanced Modeling Techniques for Global Methane Emission Forecasting Using SARIMA, LSTM, and GRU Models DOI
Ganime Tuğba ÖNDER

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 4, 2024

Abstract Forecast methods are an important aid to the early detection of future levels pollutant amounts released from global pollutants. This research predicts changes in methane gas emissions using SARIMA, LSTM, and GRU models, also compares accuracy these three prediction methods. In study, a time series analysis was conducted by focusing on monthly (CH4) emission recorded between 1984 2024. Methane data measured 2024 were used as input development models. By comparing results actual values, they evaluated with performance criteria such R², RMSE, MAE, MAPE%. The revealed that all performed well estimating emissions. SARIMA model shows best performance, followed LSTM It determined had lowest error rate 0.0020 MAPE, 0.0335 0.9998 R² values. has been estimated values may be approximately 1.5 times higher than today's level 2050.

Language: Английский

Citations

0
Energy factors affecting environmental pollution for sustainable development goals: The case of India DOI Creative Commons
Vu Ngoc Xuan

Energy Exploration & Exploitation, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 21, 2024

This study investigates the nexus between electricity consumption, fossil fuel dependency, renewable energy adoption, population growth, trade activities, economic and environmental pollution in India. The primary objective is to understand how these factors interrelate influence each other, focusing on their implications for sustainable development. used data from World Bank 2000 2023; methodology adopted includes vector autoregression modeling, Granger causality tests, cointegration analysis, impulse response functions, variance decomposition. These econometric techniques were selected due ability capture dynamic relationships, determine causality, identify long-term equilibrium among variables. findings reveal that growth significantly increases consumption usage, leading higher carbon dioxide emissions. On other hand, adoption reduces pollution. also highlights complex interplay urbanization, activities shaping India's demand outcomes. of are critical results suggest while essential, it must be balanced with practices mitigate emphasize need policy interventions promote energy, enhance efficiency, enforce regulations. Recommendations include accelerating implementing stringent efficiency standards, developing integrated policies simultaneously address economic, dimensions. actions will help India achieve a balance protection, ensuring healthier future its population.

Language: Английский

Citations

0
Impact of regional integration policy on urban ecological resilience: A case study of the Yangtze River Delta region, China DOI

Shanggang Yin,

Yijing Zhou,

Changgan Zhang

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: unknown, P. 144375 - 144375

Published: Dec. 1, 2024

Language: Английский

Citations

0
Advanced Anemia Classification Using Comprehensive Hematological Profiles and Explainable Machine Learning Approaches DOI Creative Commons
Teuku Rizky Noviandy,

Ghifari Maulana Idroes,

Rivansyah Suhendra

et al.

Infolitika Journal of Data Science, Journal Year: 2024, Volume and Issue: 2(2), P. 72 - 81

Published: Nov. 29, 2024

Anemia is a common health issue with serious clinical effects, making timely and accurate diagnosis essential to prevent complications. This study explores the use of machine learning (ML) methods classify anemia its subtypes using detailed hematological data. Six ML models were tested: Gradient Boosting, Random Forest, Naive Bayes, Logistic Regression, Support Vector Machine, K-Nearest Neighbors. The dataset was preprocessed feature standardization Synthetic Minority Oversampling Technique (SMOTE) address class imbalance. Boosting delivered highest accuracy, sensitivity, F1-score, establishing itself as top-performing model. SHapley Additive exPlanations (SHAP) analysis applied enhance model interpretability, identifying key predictive features. highlights potential explainable develop efficient, accurate, scalable tools for diagnosis, fostering improved healthcare outcomes globally.

Language: Английский

Citations

0