Analysis toolkit for clinical and genetic data.

Classifying clinical trials by trustworthiness using machine learning- quality control

Statistical analysis of a Phase III clinical trial dataset to evaluate diabetes medication effectiveness and safety.

Implement logistic regression using Python and scikit-learn to classify malignant vs. benign tumours from the Breast Cancer Wisconsin (Diagnostic) dataset

Exploratory data analysis on clinical operations using MIMIC-IV dataset, focusing on ICU utilization, vitals monitoring intensity, length of hospital stay, and in-hospital mortality.

Healthcare AI project analyzing migraine treatment outcomes using longitudinal statistical models in R.

SQL-based analysis of hospital operations, patient trends, and financial performance.

Detect HPV as the primary cause of Head and Neck Carcinoma.

This project predicts brain stroke risk using machine learning by analyzing medical and lifestyle factors. It includes data preprocessing, model training, and a simple web interface for real-time predictions. Designed for learning, research, and healthcare analytics, it demonstrates practical ML applications in disease-risk assessment.

Predictive healthcare analytics project using SPSS and Tableau to identify BP control failure risk factors and evaluate antihypertensive treatment effectiveness.

Healthcare data analytics project analysing hospital encounter data to evaluate clinical utilisation, length of stay, readmissions, payer exposure, and mortality trends.

Clinical data analytics portfolio focused on medication use, safety, and access in Canadian healthcare. Includes Python, SQL, and data visualization projects using real drug and public health datasets.

A machine learning project focused on predicting chronic kidney disease (CKD) stages and performing survival analysis using clinical biomarkers. It utilizes the Kaplan-Meier estimator to analyze patient progression and visualize survival probabilities, offering insights into CKD management.

This project focuses on developing a non-invasive prediction model that can serve as an alternative to invasive liver biopsies for assessing liver fibrosis and its level of progression in patients with Hepatitis C Virus (HCV).

Pharmacovigilance signal detection using FDA FAERS data

Alzheimer’s Disease Prediction is a classification-based machine learning project aimed at predicting the likelihood of Alzheimer’s disease in elderly individuals using clinical and demographic features.

A data science analysis of therapeutic game performance in physical rehabilitation, using score-per-minute metrics and game difficulty mapping.