Goal: Develop ML models to identify high-risk patients for hospital readmission within 30 days of discharge. Approach: Analyze clinical data, demographics, and medical history using classification algorithms to predict readmission probability. Impact: Reduce healthcare costs and improve patient outcomes through targeted interventions.

Diabetes is a medical condition that is caused due to insufficient production and secretion of insulin from the pancreas in case of Type-1 diabetes and defective response of insulin Type-2 diabetes. Diabetes is one of the most prevalent medical conditions in people today Hospital readmission for diabetic patients is a major concern in the United States. Over $250 million dollars was spent on treatment of readmitted diabetic inpatients in 2011 alone. Diabetes is chronic and does not have any specific cure. Objective:- Hospital readmission rates for certain conditions are now considered an indicator of hospital quality, and also affect the cost of care adversely. Hospital readmissions of diabetic patients are expensive as hospitals face penalties if their readmission rate is higher than expected and reflects the inadequacies in health care system. For these reasons, it is important for the hospitals to improve focus on reducing readmission rates. Identify the key factors that influence readmission for diabetes and to predict the probability of patient readmission. Approach:- The dataset chosen is that available on the UCI website which contains the patient data for the past 10 years for 130 hospitals. The code has been written in Python using different libraries like scikit-learn, seaborn, matplotlib etc. Different machine learning techniques for classification and regression like Logistic regression, Random forest etc have been used to achieve the objective. Keywords: Machine Learning, Python, scikit-learn, EDA, Healthcare

A deep-learning model that can accurately predict hospital readmission rates for patients with specific medical conditions

KNN classification of patient data to predict readmission rates to hospitals based on medical conditions and survey responses in Python.

Predicting hospital readmissions using regression models and using the chi-square test for independence to assess if initial diagnosis of diabetes has an influence on readmissions rate.

An end-to-end predictive analytics solution designed to help healthcare providers identify high-risk patients and reduce hospital readmission rates.

This was my first attempt at developing a data set to analyze and compute data of variables and their predictive values on hospital readmission rates.

The project to "Predict the Hospital Readmission Rates for Diabetic Patients and to Identify High-Risk factors", worked on during CMU Winter School (IPTSE 2014).

Hospital readmission for diabetic patients is a major concern in the United States. This disease is chronic and does not have any specific cure. Hospital readmissions are expensive as hospitals face penalties if their readmission rate is higher than expected. This study attempts to identify the key factors that influence readmission for diabetes and to predict the probability of patient readmission.

Abstract In this project, we plan to analyze the problem of predicting hospital readmission rates among diabetic patients using the "Diabetes 130-US hospitals" dataset. Tradi- tionally, this problem is dealt with by using statistical machine learning algorithms like Naive Bayes, K-Nearest Neighbors, and Logistic regression. These algorithms are known to not perform well on non-separable and high-dimensional datasets. To overcome these pitfalls, we will explore advanced techniques such as random forests, ensemble methods, and neural networks. Missing data, overfitting, and feature engineering are some of the challenges that we will encounter. The ideal outcome of the project would be to gain deeper insights into hospital readmission rates and investigate robust methods that can make improved predictions than the statistical methods. Our experiments show that Random forests performed better than other methods in the predictions.Attributes like gender, race, total number of medications, lab procedures, admission type, time in hospital of the patient had a significant influence in these predictions.

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Machine Learning assignment predicting hospital readmission rate

Predicting Hospital Readmission Rate of Diabetic Patients

This was my final capstone project from my Data Science Bootcamp at BrainStation.

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A collection of 70,000 clinical database patient records was used to predict hospital readmissions for diabetic patients using logistic regression and random forest models. Python was used to clean data and modeling was performed in R Studio.

Hospital Readmissions Reduction Program. Model developed to predict readmission rates for diabetic patients

Machine Learning-based predictive analytics project for hospital readmission rates.

Predicting hospital readmission rates for diabetic patients using Random Forest and Gradient Boosting

Predicting Hospital Readmission Rates Focus: Analyze factors that contribute to patients being readmitted to the hospital shortly after discharge.

To predict the readmission rates in a dataset from 130 US Hospital Diabetic patients.

Predictive analysis of diabetes patient hospital readmissions using machine learning to identify key factors influencing 30-day readmission rates

A streamlit app which looks at hospital readmission rates of Diabetes patients including predictive analytics

a classification model to predict 30-day hospital readmission rates for patients with diabetes using data from 130 US hospitals.

End-to-end healthcare analytics project analyzing hospital readmission and mortality measures using data cleaning, exploratory analysis, business intelligence visualizations, and machine learning to predict readmission rates.

Machine Learning-based analysis and prediction of hospital readmission rates using CMS healthcare data, including EDA, hypothesis testing, and predictive modeling.

This project explores NYC hospital database to predict the diabetes patients readmission rate to improve healthcare resource allocation.

Analyzed hospital readmission patterns across the U.S. using statistical methods and visualizations to uncover factors affecting readmission rates. Compared actual, predicted, and expected readmissions to identify gaps and highlight areas for healthcare policy improvement.