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.

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

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.

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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.

A Disease Risk Prediction System utilizes machine learning and data analysis to forecast an individual's likelihood of developing chronic diseases by analyzing electronic health records, genetic data, and lifestyle factors, enabling early intervention and personalized care that improves patient outcomes and reduces healthcare costs.

Chronic Kidney Disease Prediction is a machine learning-based healthcare project that analyzes patient medical data to predict the risk of CKD. It uses data preprocessing, feature analysis, and classification models to provide accurate predictions, helping in early detection and better clinical decision-making.

Patient no-show prediction analysis using real clinic appointment data (52,973 records). Identifies key risk factors including chronic disease status, insurance type, and confirmation reminders. Builds and tunes a machine learning model achieving 86% accuracy and 0.84 ROC-AUC. Includes a browser-based daily risk tool for front desk staff.

Chronic Kidney Disease (CKD) is a serious condition where kidney function slowly declines. Early stages show few symptoms, causing late diagnosis. Current detection depends on manual analysis of clinical and lab data, which is time-consuming. An automated intelligent system can support early prediction and risk assessment.