Learning-focused exploratory data analysis of chronic disease indicators using Python

End-to-end machine learning project involving exploratory data analysis, preprocessing, and model development using the U.S. Chronic Disease Indicators dataset. Implements supervised and unsupervised learning techniques, compares model performance using appropriate evaluation metrics, and derives data-driven insights from healthcare data.

Using the CDCs Chronic Disease Indicators (CDI) dataset to explore how behaviors and socioeconomic conditions intersect with chronic disease burden across U.S. populations.

Advanced Data Mining Analysis on Chronic Disease Indicators Community Analysis

This project analyzes public health data on chronic diseases impacting the U.S. It explores trends and risk factors using the CDC's Chronic Disease Indicators (CDIs) to inform public health interventions.

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Association analysis: farmers market frequency and chronic disease indicators

The goal is to develop robust predictive models to identify patterns in chronic disease data, aiding public health planning and interventions. This project aims to enhance predictive modeling for chronic diseases, leading to better preventative strategies and health outcomes.

The goal is to develop robust predictive models to identify patterns in chronic disease data, aiding public health planning and interventions. This project aims to enhance predictive modeling for chronic diseases, leading to better preventative strategies and health outcomes.

A comprehensive Python-based analytical system for exploring chronic disease indicators from the Centers for Disease Control and Prevention (CDC). Built for the EDAV course with production-ready code, extensive visualizations, and in-depth statistical analysis.

Exploratory Data Analysis and Dashboard on U.S. Chronic Disease Indicators

I explored a real-world health dataset using machine learning to uncover insights and patterns. The project includes regression, classification, clustering, correlation analysis, and model evaluation with clear visualizations—strengthening my Python, data analysis, and ML skills.

R-Studio RMarkdown analysis of public dataset of US Territory Chronic Disease Indicators

Doing a long-form analysis of the U.S. Chronic Disease Indicators (CDI) dataset found here [https://www.kaggle.com/datasets/utkarshx27/us-chronic-disease-indicators-cdi]

A data analysis to discover possible trends in chronic disease indicators and land usage.

US Chronic Diseases Indicators dataset comes from CDC's Division of Population Health that provides cross-cutting set of 124 indicators that were developed by consensus and that allows states and territories and large metropolitan areas to uniformly define, collect, and report chronic disease data that are important to public health practice and available for states, territories and large metropolitan areas. In addition to providing access to state specific indicator data, the CDI web site serves as a gateway to additional information and data resources. The CDI website enables public health professionals and policymakers to retrieve uniformly defined state-level and selected metropolitan-level data for chronic diseases and risk factors that have a substantial impact on public health. These indicators are essential for surveillance, prioritization, and evaluation of public health interventions for chronic disease.

In healthcare domain, large volumes of data are generated which is coined by “3 V’s” Volume, Velocity, and Variety. Chronic diseases are an important public health problem, which can result in morbidity, mortality, disability, and decreased quality of life. In this research, big data technique such as R is used to analyze US Chronic Disease Indicators (CDI) that allows states and territories and large metropolitan areas to uniformly define, collect, and report chronic disease data. Analysis shows that highest percentage of alcohol use among youth is seen for Texas and lowest percentage is seen for District of Columbia. The results indicate that Diabetes is the most prevalent disease among top 10 US chronic diseases. Based on the comparison, diabetes prevalence among women aged 18-44 years having sum less than 5000 depicts that Washington state is the highest among all states.

Workshop that guides participants through the thought process for a data analysis of chronic disease indicators

Analysis of obesity, diabetes and low physical activity in the United States using CDC Chronic Disease Indicators.

ETL pipeline and analysis of EU27 health indicators, including population, life expectancy, health spending, mortality, and chronic diseases.

Exploratory Data Analysis on a Chronic Kidney Disease dataset to uncover key health indicators, data quality issues, and patterns in CKD diagnosis.

Chronic Disease Indicator dataset project. Public health analysis of the United States based on various CDIs provided by the CDC.

Analysis of U.S. Chronic Disease Indicators data (2019–2022) focusing on adult females with any disability. Includes visual trends and insights using Python and pandas.

Interactive data science project using CDC Chronic Disease Indicators to explore state-level trends in chronic conditions across the U.S. The dashboard enables geographic and temporal analysis, while time series models forecast short-term disease prevalence to support data-driven public health insights.

In my recent exploration of the U.S. Chronic Disease Indicators dataset, I dived deep into data cleaning, statistical analysis, and visual storytelling to uncover health trends across states, especially focusing on Diabetes and Obesity.

Healthcare Data Analysis – Patient Dataset This project analyzes a healthcare dataset containing patient visits, medical indicators, symptoms, smoking behavior, and chronic disease information. The goal is to extract meaningful insights that can help in understanding health patterns among patients.

This project explores the relationship between chronic illness and psychological distress (depression and anxiety) using global health datasets. It applies statistical analysis and machine learning models to predict mental health outcomes based on disease burden, treatment access, and health indicators.

Chronic Kidney Disease (CKD) Data Analysis using Python. This project analyzes clinical CKD data through data cleaning, missing value imputation, correlation analysis, visualization, and basic predictive modelling. It applies Python and statistical methods to explore clinical indicators and supports SDG 3: Good Health and Well-Being.

Chronic Kidney Disease (CKD) Data Analysis using Python. This project analyzes clinical CKD data through data cleaning, missing value imputation, correlation analysis, visualization, and basic predictive modelling. It applies Python and statistical methods to explore clinical indicators and supports SDG 3: Good Health and Well-Being.

Voice Biomarker Monitor : An advanced speech analysis system that evaluates voice features like pitch, jitter, and shimmer to identify early indicators of chronic conditions such as Parkinson’s disease, COPD, and heart failure, providing timely risk alerts and supporting remote health monitoring.