Machine learning predicts who will decline faster in Alzheimer’s disease using routine clinic data
Researchers developed and validated ElasticNet machine learning models that predict 12-month MMSE and BADL outcomes in ...
Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...
Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...
Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.
Machine learning can predict many things, but can it predict who will develop schizophrenia years before the average diagnosis time?
Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
We conducted a retrospective, country-level study integrating GLOBOCAN 2022 incidence and mortality data, ClinicalTrials.gov records (2004-2025), and socioeconomic indicators (United Nations ...
Tree-based ensemble models often outperform more complex deep learning architectures when applied to structured, tabular IoT data. While neural networks excel with image and unstructured inputs, ...
Plants are constantly exposed to a wide array of biotic and abiotic stresses in their natural environments, posing ...
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