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What kind of fiber pills should I take?

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Diabetes mellitus

Polyacrylonitrile

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Journal Article•DOI Healthy diet and fiber intake are associated with decreased risk of incident symptomatic peripheral artery disease – A prospective cohort study: Andrea Kulezic, Sara Bergwall, Shahab Fatemi, Emily Sonestedt, Moncef Zarrouk, Anders Gottsäter, Stefan Acosta - Show less +6 more 20 Aug 2019-Vascular Medicine 19 Citations	Primary prevention programs directed against PAD should therefore include a fiber recommendation.
Journal Article•DOI Three-dimensionally braided carbon fiber–epoxy composites, a new type of materials for osteosynthesis devices. II. Influence of fiber surface treatment Y. Z. Wan, Y. L. Wang, F. G. Zhou, G. X. Cheng, K. Y. Han - Show less +4 more 01 Aug 2002-Journal of Applied Polymer Science 30 Citations	A trade-off between the fiber–matrix bond and fiber strength loss should be considered.
Journal Article•DOI Assessments of lung digestion methods for recovery of fibers. David B. Warheit, Harry C. Hwang, Lena Achinko - Show less +2 more 01 Apr 1991-Environmental Research 11 Citations	These results indicate that lung digestion methods should be carefully assessed for each fiber type before initiating fiber clearance studies.
Open access•Journal Article•DOI Thermal threshold: research study on small fiber dysfunction in distal diabetic polyneuropathy. Pedro Jimenez-Cohl, Carlos Grekin, Cristian E. Leyton, Claudio Vargas, Roberto Villaseca - Show less +4 more 01 Jan 2012-Journal of diabetes science and technology 22 Citations	We suggest that small fiber should be studied before large fiber function to diagnosis distal and symmetrical DN.

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When does random forest outperform xgboost?

Random forest outperforms XGBoost in various scenarios based on the data from the provided contexts. In the context of diabetes risk prediction, random forest showed higher prediction accuracy compared to XGBoost. Additionally, in the context of urban forest classification, the Extreme Gradient Boosting (XGBoost) classifier outperformed other classifiers, indicating that random forest may excel in different scenarios. Moreover, in the context of electric porcelain firing kiln process fault diagnosis, XGBoost outperformed traditional classifiers, showcasing the potential for random forest to outperform XGBoost in similar fault diagnosis tasks. Furthermore, in the context of supply fraud prediction, the proposed XGBoost and random forest algorithm demonstrated greater efficiency compared to standalone XGBoost and random forest algorithms. Therefore, random forest may outperform XGBoost in tasks where it shows higher prediction accuracy, efficiency, and fault diagnosis rates.