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    Prediction of Anemia from Multi-Data Attribute Co-Existence by Talal Qadah, Asmaa Munshi

    Published 2024-01-01
    “…Therefore, this study has reevaluated the claims within the domain of detecting and predicting anemia with the best machine learning algorithm. Another research problem, lies with the fact that previous studies on anemia prediction utilized limited machine learning algorithms across a narrow range of datasets, whereas this current study employed numerous machine learning algorithms across a wide range of anemia datasets and tested three hypotheses. …”
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  3. 63

    Early Detection of Parkinson's Disease: Ensemble Learning for Improved Diagnosis by Raut Komal, Balpande Vijaya

    Published 2025-01-01
    “…This paper proposed several machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression and Support Vector Machine and design an ensemble of these models to detect and classify Parkinson's disease. …”
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  4. 64

    Comparative study on Functional Machine learning and Statistical Methods in Disease detection and Weed Removal for Enhanced Agricultural Yield by Sudha D., Menaga D.

    Published 2023-01-01
    “…The technology has developed to rectify the problems using some machine learning algorithms like Random Forest algorithms, Decision trees, Naïve Bayes, KNN, K-Means clustering, Support vector machines. …”
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    Fault Detection in Photovoltaic Systems Using a Machine Learning Approach by Jossias Zwirtes, Fausto Bastos Libano, Luis Alvaro de Lima Silva, and Edison Pignaton de Freitas

    Published 2025-01-01
    “…The proposed fault detection solutions rely on analyzing different algorithms, including Support Vector Machine, Artificial Neural Network, Random Forest, Decision Tree, and Logistic Regression. …”
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    Changes Detection of Mangrove Vegetation Area in Banyak Islands Marine Natural Park, Sumatra, Southeast Asia by Muhammad Arif Nasution, Helmy Akbar, Singgih Afifa Putra, Ammar AL-Farga, Esraa E. Ammar, Yudi Setiawan

    Published 2025-01-01
    “…Spectral index combinations, including NDVI, NDMI, MNDWI, and MVI, were analyzed using random forest classification, a tree-based machine learning algorithm. …”
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    A machine learning model for early detection of sexually transmitted infections by Juma Shija, Judith Leo, Elizabeth Mkoba

    Published 2025-06-01
    “…The dataset was split into a 70%:15%:15% ratio for training, testing, and validation, respectively, and five machine learning algorithms were evaluated: AdaBoost, Support Vector Machine, Random Forest, Decision Tree, and Stochastic Gradient Descent. …”
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    Detection and Analysis of Malicious Software Using Machine Learning Models by Selman Hızal, Ahmet Öztürk

    Published 2024-08-01
    “…Our analysis encompasses binary and multi-class classification tasks under various experimental conditions, including percentage splits and 10-fold cross-validation. The evaluated algorithms include Random Tree (RT), Random Forest (RF), J-48 (C4.5), Naive Bayes (NB), and XGBoost. …”
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