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.

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Analyzing CKD Data-set to predict if the patient has CKD diseases based on all the factors.

Statistical analysis of Chronic Kidney Disease (CKD) risk factors using chi-square tests, correlation, and regression with R and Excel.

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.

Multimodal molecular imaging-based statistical connectomic analysis of inter-regional brain correlations derived from ¹⁸F-FDG PET and ⁹⁹ᵐTc-DTPA SPECT in a murine model of chronic kidney disease (CKD)

This proposed work aims to identify ckd, the initial step is to preprocess the data by removing Nan values, and second step is data visualization to improve the statistical analysis of the data.

Tools Used - Logistic Regression, Python, R, Microsoft Excel #The objective was to use statistical analysis to identify the key parameters of Chronic Kidney Disease upon which this model can be used a screening tool to identify patient's risk for CKD #A dataset with patient demographics and clinical parameters of around 8000 patients was cleaned to address null values and outliers #The issue of class imbalance was addressed by stochastic sampling method #Logistic Regression was then used to identify key variables that contribute to the development of Chronic Kidney Disease #Since recall of the model was more important than accuracy and precision, receiver operating characteristic (ROC) curve was used to pick the right threshold for probability