This project focuses on data preprocessing and epilepsy seizure prediction using the CHB-MIT EEG dataset. It includes steps like data cleansing, feature extraction, and handling imbalanced datasets, aimed at improving the accuracy of seizure prediction.

A curated dataset of Indian medicines, organized by brand. Essential for healthcare research and pharmaceutical analysis.

EDA on medical insurance data using Python and R to explore how age, BMI, smoking, and other factors influence claim amounts and health risk profiles.

MCP server providing healthcare analytics capabilities for Smartsheet, including clinical note summarization, patient feedback analysis, and research impact assessment

An interactive Tableau dashboard promoting nutritional health through the exploration of micronutrients

# Notes MCPAn MCP server that connects with your Apple Notes on macOS. 📓 This tool allows you to manage your notes efficiently with simple commands. 🛠️

medical appointments No-Show classifier

Heart disease is still a major worldwide health concern since it is one of the leading causes of mortality and morbidity in India. Early and precise diagnosis of heart disease can save lives and reduce medical costs. Conventional diagnostic methods, however, are often expensive and need specific equipment and expertise.

🩺 Healthcare Data Integration, Analysis & Visualization project using Python, Jupyter Notebook, and Power BI. Integrates multi-sheet Excel data, performs data cleaning, feature engineering, exploratory analysis, and delivers interactive healthcare dashboards.

Hospital database system built with Oracle APEX and SQL, featuring an interactive dashboard for real-time insights into patient distribution and doctor availability. Designed to optimize resource management and support hospital administration in data-driven decision-making.

Dataset release in association with 10.3389/fimag.2025.1547166

Machine learning project using decision trees to predict stroke risk from healthcare data. Includes a R Markdown file with full code, along with a case study summary.

This project forecasts monthly healthcare call volumes using time-series modeling (ARIMA), optimizing call center staffing and reducing wait times. Features include trend analysis, model tuning, and an interactive dashboard for real-time insights.

This project explores and compares two diabetes-related datasets using data preprocessing, visualization, and machine learning models. It includes performance evaluation of classifiers like Logistic Regression and Random Forest to identify key factors influencing diabetes risk and improve prediction accuracy.

Healthcare Analysis Report using Tableau: * This report compares the diabetic and non-diabetic patients, with 34.90% being diabetic. * It summarizes the patient's blood pressure, including a count for elevated, high, low, and normal BP patients. * The report also presents the BMI distribution and BMI by Age group for the patients.

Predicted stroke risk using patient data to support early healthcare interventions.

Interactive Power BI dashboard for analyzing healthcare facility data. This project provides comprehensive insights into patient demographics, visit trends, wait times, departmental referrals, and satisfaction levels. Designed to help healthcare administrators make data-driven decisions for improving operational efficiency and patient experience.

Machine Learning project using classification, regression, and ensemble techniques to predict breast cancer mortality status and survival months using clinical data. Built with scikit-learn, decision trees, logistic regression, and Naïve Bayes. Includes detailed model evaluation, data preprocessing, and interpretability.

Clinical data warehouse with SQL analytics for patient encounters, medications, labs & nursing workflows. Features HIPAA-compliant synthetic data.

This project aims to predict the likelihood of a heart attack based on various health indicators using machine learning techniques. The dataset used contains patient data with features such as age, cholesterol levels, blood pressure, and more.