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.

Data mining projects include predicting risk score of chronic diseases with NHANES data and analysis of patient and insurance claim data.

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.

Exploratory Data Analysis of Chronic Disease dataset using Python, Pandas, Seaborn, and Matplotlib to uncover health patterns and risk factors.

The data is the largest health survey conducted by CDC (Centers for Disease Control and Prevention) across 50 states in USA.The objective was to conduct an exploratory analysis to understand factors influencing health related risk behaviors. Analyzed factors like food habits and activity that influence health related risks using techniques like linear regression, cluster analysis such as k-means and Hierarchical clustering methods in R. Performed data cleansing in R and implemented classification technique like decision tree approach to classify different chronic diseases.

Machinery learning is a fast-expanding area that will change the method for the diagnosis and management of this chronic condition by applying itself to diabetes as a global pandemic. Machine learning principles have been used to build algorithms to help predictive models of the likelihood of diabetes development or related complications. Digital therapy has shown to be a well-established lifestyle care intervention for diabetes control. Patients are becoming more self-managed, and the assistance of therapeutic decision-making is available to both them and health care practitioners. Machine learning helps patient signs and bio-markers to persist, unburdened, remotely controlled. Social networking and online forums also increase patient commitment to the treatment of diabetes. Development in technologies helped to optimize the use of diabetes tools. These smart technological reforms together have led to an improved glycemic regulation, a decrease in fast glucose and glycosylated hemoglobin levels. Machine learning introduces a change in diabetes treatment model from traditional management techniques to data-driven care growth The trouble with medicines is that various drug formulations can cure the condition in several ways. As the diabetic population grows, new medications are increasingly emerging. In order to treat common diseases such as elevated cholesterol and high blood pressure, diabetics also continue to take other drugs. With the patient's age and other physical conditions, the potency of these medicines varies In this method, the effectiveness, risks of side effects and costs are measured side by side, and are readily grasped by doctors and patients. The most prevalent form of Type 2 diabetes effects more people as people grow up. This disease has also escalated dramatically due to the spread of western diets and lifestyles to developing countries. Diabetes is an incurable metabolic illness that happens when high blood sugar is present, and may have deadly effects. Today, medicine, nutritious diets and exercise will regulate diabetes. It is also unpredictable to choose and administer the most appropriate mixture of prescription, which is stable, cheap and well tolerated by patients as well By applying an adequate methodology for the design and development of systems experts can achieve objectives satisfactorily, as in the case of the Weiss and Kuligowski methodology. On the other hand, machine learning has several knowledge machine algorithms, which can be useful to be applied on various data sets through the different interfaces that offers, as the option of Explorer and Datasets, which were worked in this case of study, or to be included in other applications. Furthermore, both tools, contain what is necessary to conduct data transformations, grouping, regression, clustering, correlation and visualization tasks. Because they are designed as extensibility-oriented tools which allows to add new functionalities to a project, because it can be combined with other programming languages such as Prolog, for generation more robust expert systems Readmitted diabetes patients Machine learning techniques allow to automatically identify patterns and even make predictions based on a large amount of data that could be extracted from the computer systems used to ascertain information on readmission of diabetes patients. The analysis Clustering or grouping is a technique that allows exploring a setoff objects to determine if there are groups that can be significantly represented by certain characteristics, in this way, objects of the same group are very similar to each other and different from objects in other groups. The results obtained by comparing the relevance of different attributes as well as the use of two of the most popular algorithms in the world of machine learning are presented: neural networks and decision trees. Automatic classification of blood glucose measurements will allow specialists to prescribe a more accurate treatment based on the information obtained directly from the patients' glucometer (Hosseini et al, 2020). Thus, it contributes to the development of automatic decision support systems for gestational diabetes. This high level of glucose in the blood is transferred to the fetus causing various disorders: excessive growth of adipose tissues, which increases the need for caesarean section, neonatal hypoglycemia and increased risk of intrauterine fetal death (Dagliati et al, 2018). It also increases the risk of type 2 diabetes once the gestation period is over for both the mother and the fetus. The project proposes the development of intelligent and educational tools for the survey based on neurodiffuse techniques integrated into a telemedicine system. Telemedicine systems have been used with success on numerous occasions in diabetes and the integration of decision support tools in this type of system helps a better interpretation of the data (Abhari et al, 2019).

AI-based chronic disease risk prediction system with explainability, uncertainty quantification, and bias analysis

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".

Heart Disease is one of the most prevalent chronic diseases in the United States, impacting millions of Americans each year and exerting a significant financial burden on the economy. The purpose of this project is to use data analysis techniques to identify groups with the highest risk of heart disease and use the shared characteristics of these groups to predict whether someone is at risk of heart disease. We will first use contingency tables, conditional entropy, and odds ratios to identify at-risk groups and key predictor variables; more detail regarding at-risk groups will come from crossing multiple variables, and seeing which variables recur in the most-significantly-different groupings. Then, we will create a set of decision rules to determine whether a new respondent is at risk of heart disease. We will then compare this classifier’s performance with that of a gradient boosted decision tree algorithm. We will also be using a general linear model to determine which predictor variables play the most importance when it comes to heart problems within patients. Tests will be conducted with a focus on AIC, logarithmic odds, summary statistics, and plots to aid in our determination of a best general linear model.

The Medical diseases analysis is emerging in the area of research. In recent years, various attempts are made for the creation of computer aided diagnosis applications. Due to errors in medical diagnostics systems can result in seriously misleading the treatment of patients. Machine learning finds various applications in the areas including computer aided diagnosis. After converting subject in equation disease can be indicated accurately. For the analysis of multi model bio medical data, machine learning offer the convenient approach for making classy and automatic algorithms. This project provides the comparative analysis of different machine learning algorithms for detection of liver disease. The liver diseases are one of the most prevalent chronic diseases, worldwide. It is proved to be based on multi factors caused by complex interactions involving the genetic, epigenetic and environmental factors. This project demonstrate and analytical approach for prediction of liver diseases in patients using probabilistic model based on Artificial Neural network (ANN), KSVM, SVM, Naïve Baye’s. The technique used for classification and prediction are based on recognizing typical and diagnostically most important clinical features considered responsible for Liver diseases. These clinical features are provided as input to the classification model for prediction and qualitative analysis. The main contribution of project involve developing of classifier model based on the above mentioned machine learning algorithms. The analysis confirmed high risk and low risk patients based on the predictions by the probabilistic model. The qualitative parameters involved in the research are Accuracy, Specificity and Sensitivity.

Data-Driven Disease Prediction: A Chronic Risk Analysis predicts hypertension, diabetes, heart disease & CKD using advanced analytics. Enriched from Kaggle’s 100K dataset, it applies PCA, Isolation Forest & RFE with multi-target classification and ML models to yield actionable healthcare insights.

No description available

An analysis of Risk Factors in Chronic Kidney Disease

No description available

Design and implementation of chronic disease risk analysis method based on crowd cohort simulation.

No description available

No description available

Scripts for single-cell analysis for manuscript "Multi-Scalar Data Integration Decoding Risk Genes for Chronic Kidney Disease"

“Healthcare Capacity and Chronic Disease Outcomes in BRICS: A Predictive, Classification, and Clustering Analysis of Recovery Rates and Mortality Risk”

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

This repository contains an Exploratory Data Analysis (EDA) of a chronic disease dataset. The goal of this analysis is to identify key risk factors, trends, and patterns related to chronic diseases, helping in early detection and prevention strategies.

This project performs Exploratory Data Analysis (EDA) on a Chronic Kidney Disease dataset to identify key patterns, relationships, and potential risk factors associated with the disease.

Machine learning analysis of BRFSS health survey data to identify behavioral clusters and predict chronic disease risk patterns for population health insights

End-to-end SQL analysis of patient demographics, admissions, chronic disease prevalence, lifestyle risk factors, and doctor performance at a specialist hospital.

This repository contains an Exploratory Data Analysis (EDA) on chronic diseases data. The objective is to uncover insights and patterns that can help in understanding the prevalence, risk factors, and impact of chronic diseases. The analysis includes data cleaning, visualization, and statistical analysis to explore various factors affecting .

Reproducible analysis of risk of graft failure in chronic kidney disease patients using joint models for longitudinal and survival data. The project compares GFR, haematocrit, and proteinuria to evaluate which biomarker best predicts disease progression.

Exploratory Data Analysis (EDA) on chronic disease dataset using Python to analyze health patterns and risk factors. Includes data cleaning, visualization, and insight generation using Pandas, Matplotlib, and Seaborn.

This project predicts the likelihood of chronic diseases like diabetes, heart disease, and kidney disease using patient medical data. It involves data cleaning, exploratory analysis, feature engineering, and machine learning modeling. Key insights and feature importance highlight critical risk factors for early intervention.

SQL Healthcare Data Analysis demonstrates the application of SQL for analyzing healthcare datasets to optimize patient care and operational workflows. Key analyses include risk stratification for diabetes, chronic disease cohort analysis, healthcare workflow optimization, readmission analysis, and appointment distribut

Health-Risk-Factor-Analysis uses statistical methods and R programming to examine how age, BMI, blood pressure, cholesterol, and lifestyle factors influence the risk of chronic diseases. The project reveals trends and insights to support early intervention and preventive healthcare decisions.