BACKGROUND: Parkinson's disease (PD) is a prevalent neurodegenerative condition, the effect of obesity on PD remains controversial. We aimed to investigate the associations of obesity patterns on PD and all-cause mortality, while developing machine l...
OBJECTIVES: This study aimed to develop an accurate prediction model for the risk of Non-alcoholic fatty liver disease (NAFLD) using the random survival forests (RSF), and to investigate the distribution of NAFLD risk with time.
Cancer causes over 10 million deaths annually worldwide, with 40.5% of Americans expected to be diagnosed in their lifetime. Early detection is critical; for liver cancer, survival rates improve from 4 to 37% when caught early. However, predicting ti...
BACKGROUND: To investigate the association between the Dietary Inflammatory Index (DII), biological aging, and the staging and mortality of cardiovascular-kidney-metabolic (CKM) syndrome.
Prostate cancer bone metastasis (PCBM) is a highly lethal condition with limited survival. Accurate survival prediction is essential for managing these typically incurable patients. However, existing clinical models lack precision. This study seeks t...
The ratio of hemoglobin (Hb) to red blood cell distribution width (RDW), known as HRR, functions as an innovative indicator related to prognosis. However, whether HRR can predict the mortality for pulmonary embolism (PE) patients remains ambiguous. A...
Cervical cancer (CC) is a major cause of mortality in women, with stagnant survival rates, highlighting the need for improved prognostic models. This study aims to develop and compare machine learning models for predicting five-year cause-specific su...
Visceral Leishmaniasis (VL), also known as Kala-Azar, poses a significant global public health challenge and is a neglected disease, with relapses and treatment failures leading to increased morbidity and mortality. This study introduces an explainab...
Sepsis is a life-threatening condition that presents substantial challenges to healthcare and pharmacological management due to its high mortality rates and complex patient responses. Accurately predicting patient outcomes is essential for optimizing...
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