Obesity

Regulatory risk loci link disrupted androgen response to pathophysiology of Polycystic Ovary Syndrome

A major challenge in deciphering the complex genetic landscape of Polycystic Ovary Syndrome (PCOS) lies in the limited understanding of how susceptibility loci drive molecular mechanisms across diverse phenotypes. To address this, we integrated molecular and epigenomic annotations from proposed causal cell-types and employed a deep learning (DL) framework to predict cell-type-specific regulatory e...

Exploring Novel Biomarkers for Early Detection of Osteoporosis

Osteoporosis is characterized by diminished BMD and deteriorated bone microstructure, significantly increasing fracture susceptibility. This study leverages machine learning for identifying key predictors of osteoporosis, potentially improving non-invasive screening tools. We utilized the “Bone Mineral Density” dataset from Harvard University, categorizing variables into demographics, bone loss pr...

Enhancing Mental Health Decision-Making with Artificial Intelligence/Machine Learning: A Prescriptive Analytics Approach for Customised Outcomes

Depression is a complex and widespread mental health condition affecting over 280 million people globally, yet access to timely diagnosis and personal...

Enhancing Fairness in Diabetes Prediction Systems through Smart User Interface Design

Artificial intelligence (AI) in chronic disease prediction often exhibits algorithmic biases, hindering equitable healthcare delivery. This study aims...

Assessment and Prediction of Clinical Outcomes for ICU-Admitted Patients Diagnosed with Hepatitis: Integrating Sociodemographic and Comorbidity Data

Hepatitis, a disease characterized by inflammation of the liver, is a leading global health challenge that contributes to over 1.3 million deaths annu...

Deep learning-enabled MRI phenotyping uncovers regional body composition heterogeneity and disease associations in two European population cohorts

Body mass index (BMI) does not account for substantial inter-individual differences in regional fat and muscle compartments, which are relevant for th...

Bridging the Heterogeneity of Myasthenia Gravis Severity Scores for Digital Twin Development

Myasthenia gravis (MG) is a rare autoimmune neuromuscular disease. Clinical trials with rigorously collected data, especially for rare diseases, provi...

Modeling the Impact of Social Determinants on Breast Cancer Screening: A Data-Driven Approach

This study addresses the critical implementation science challenge of operationalizing social determinants of health (SDoH) in clinical practice. We d...

Application of Machine Learning Approaches to Develop Predictive Models for Diabetes and Hypertension among Bangladesh Adults

With rapid urbanization, lifestyle changes, and an aging population, non-communicable diseases (NCDs), including hypertension and diabetes, pose signi...

Artificial Intelligence Enabled Phenogrouping of Heart Failure with Preserved Ejection Fraction Depicts Early and End-Stage Trajectories

Heart failure with preserved ejection fraction is challenging to diagnose, precluding the initiation of prognostic medications. A deeper understanding...

AI-Driven Personalization of Dual Antiplatelet Therapy Duration Post-PCI: A Novel Approach Balancing Ischemic and Bleeding Risks

Precision-guided dual antiplatelet therapy (DAPT) duration post-percutaneous coronary intervention (PCI) remains a clinical challenge. Current risk st...

Hybrid Fuzzy Logic and Logistic Regression Model with Recursive Feature Elimination for Enhanced Prediction and Clinical Decision Support in Type 2 Diabetes Mellitus Among Adults Aged 35 to 45

This paper presents a hybrid model combining fuzzy logic, recursive feature elimination (RFE), and logistic regression to predict type 2 diabetes mell...

Development of a pilot machine learning model to predict successful cure in critically ill patients with community-acquired pneumonia

Severe community-acquired pneumonia (CAP) remains a major cause of critical illness, yet there are no validated early clinical criteria to predict sho...

Machine Learning Fairness in Predicting Underweight, Overweight and Adiposity Across Socioeconomic and Caste Group in India: Evidence from the Longitudinal Ageing Study in India

Machine learning (ML) models are widely used to predict body mass index (BMI), yet their fairness across socioeconomic and caste groups remains uncert...

CLINICAL VALIDATION OF SWAASA ARTIFICIAL INTELLIGENCE PLATFORM USING COUGH SOUNDS FOR SCREENING AND DIAGNOSIS OF RESPIRATORY DISEASES

Analysis of cough sounds have the potential to give a clue regarding the underlying respiratory disease. The Swaasa AI platform using artificial intel...

Genomics reveals eleven obesity endotypes with distinct biological and phenotypic signatures

Obesity, a leading global risk factor for cardiometabolic conditions, arises from multifaceted and biologically complex mechanisms1,2. To elucidate th...

Longitudinal Prediction of BMI using Explainable AI: Integrating Polygenic Scores, Maternal, Early-Life and Familial Factors

This study aimed to predict body mass index (BMI) trajectories from childhood to early adulthood using explainable artificial intelligence, integratin...

AI-based Hepatic Steatosis Detection and Integrated Hepatic Assessment from Cardiac CT Attenuation Scans Enhances All-cause Mortality Risk Stratification: A Multi-center Study

Hepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under-diagnosed in patients with suspected or known coronary art...

“A Population-Specific Breast Cancer Risk Prediction Model for Indian Women (A Pilot Study): Advancing Beyond Traditional Assessment Tools”

Breast cancer is the most prevalent cancer among women in India, characterized by late-stage diagnoses and high mortality rates. Existing breast cance...

A Data-Driven Approach to Polycystic Ovary Syndrome Diagnosis: Evaluating Machine Learning Models

PCOS is recognized as a major health concern affecting women around the world. Early detection and treatment of PCOS significantly reduce implications...