Being the most common and rapidly growing disease, Diabetes affecting a huge number of people from all span of ages each year that reduces the lifespan. Having a high affecting rate, it increases the significance of initial diagnosis. Diabetes brings other complicated complications like cardiovascular disease, kidney failure, stroke, damaging the vital organs etc. Early diagnosis of diabetes reduces the likelihood of transiting it into a chronic and severe state. The identification and analysis of risk factors of different spinal attributes help to identify the prevalence of diabetes in medical diagnosis. The prevalence measure and identification of diabetes in the early stages reduce the chances of future complications. In this research, the collective NHANES dataset of 1999-2000 to 2015-2016 was used and the purposes of this research were to analyze and ascertain the potential risk factors correlated with diabetes by using Logistic Regression, ANOVA and also to identify the abnormalities by using multiple supervised machine learning algorithms. Class imbalance, outlier problems were handled and experimental results show that age, blood-related diabetes, cholesterol and BMI are the most significant risk factors that associated with diabetes. Along with this, the highest accuracy score .90 was achieved with the random forest classification method.

This project aims to explore clinical and laboratory features associated with chronic kidney disease (CKD) and to identify key predictors that distinguish CKD from non-CKD individuals.

The Chronic Kidney Disease Predictor is a machine learning project that offers early detection, accurate prediction, and risk assessment of chronic kidney disease. It utilizes patient data analysis, provides a user-friendly interface, and serves as a valuable decision support tool for healthcare professionals.

This report explores the development of a predictive model for chronic kidney disease (CKD) using advanced data analysis and machine learning techniques.

This repository contains the analysis code for the paper titled "The spatially resolved transcriptome signatures of glomeruli in chronic kidney disease" by Hasmik Soloyan et al. 2022.

Machine Learning project for predicting chronic kidney disease using the Random Forest, KNN, Decision Tree and Regression algorithms. Includes data preprocessing, model training, evaluation, and performance analysis based on a real-world dataset.

No description available

Nephron AI is a project by Ayush Anand. This is a Machine Learning powered toll for diagnosis and analysis of causes for Chronic Kidney Disease with 99.9% accuracy.

Designed a Gaussian Bayesian Network (GBN) to evaluate Chronic Kidney Disease using patient health data, facilitating probabilistic predictions of disease risk based on overall health indicators

In this project, I focused to analyze and visualize the dataset using python language to understand what the data looks like, show the relationship between the data, and choose the best way to clean the dataset. Also, we applied a different machine learning algorithm to predict the test data set.

Analysis of the data for a epidemiological study for the Chronic kidney disease. Identification of the correlation between blood test, urine test, age, diabetes with Chronic kidney failure.

I have uploaded my Chronic Kidney Disease Prediction project in this repository. This project is part of my learning journey and focuses on predicting kidney disease using data analysis and machine learning.

STAR-Echo: A Novel Biomarker for Prognosis of MACE in Chronic Kidney Disease Patients using Spatiotemporal Analysis and Transformer-Based Radiomics Models.

This repository contains analysis code for the research paper: "Developing a new albuminuria-free risk prediction equation for kidney failure in patients with chronic kidney disease: retrospective cohort study".

Classification of people with a high probability of contracting chronic kidney disease(CKD). (R-Studio, RapidMiner, Logistic regression, Linear Discriminant Analysis) . (R-Studio, RapidMiner, Logistic regression, Linear Discriminant Analysis)

Reproducible statistical analysis of a Chronic Kidney Disease (CKD) dataset using R and Python, including hypothesis testing, multivariate logistic regression, and robustness evaluation under MCAR/MNAR missing data mechanisms.

This Streamlit app uses AI and ML to analyze report. It supports document uploads for report analysis. The app also uses ML models to classify chronic diseases like brain tumors, kidney stones, and pneumonia.

A complete EDA and Machine Learning analysis on Chronic Kidney Disease data. Includes data cleaning, feature encoding, scaling, PCA, clustering, and classification using Logistic Regression, SVM, KNN, Random Forest. Visual insights built with Seaborn and Plotly to identify key biomarkers.

Chronic kidney disease analysis

Chronic Kidney Disease Analysis

Chronic kidney disease analysis

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This project improves early detection and risk stratification of Chronic Kidney Disease. Using clinical, demographic, and lifestyle data, the study demonstrates how predictive modeling can enhance patient risk assessment and support proactive interventions.

Chronic Kidney Disease Analysis Using machine Learning

IBM internship project

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Chronic Kidney Disease Analysis Using machine Learning

Chronic Kidney Disease Analysis Using machine Learning

Chronic Kidney Disease Analysis Using machine Learning