No description available

No description available

Introduction In my case studies I keep writing in English because it is used in Kaggle and I also keep them in Portuguese because my native language is Brazilian Portuguese, so we can share more knowledge and experiences in Kaggle with our Brazilian colleagues. We will develop and analyze the algorithms with the best capacity and identify the problems in the heart and at the end we will make a comparison between them. Description Context Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict a possible heart disease. People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help. Attribute Information Age: age of the patient [years] Sex: sex of the patient [M: Male, F: Female] ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic] RestingBP: resting blood pressure [mm Hg] Cholesterol: serum cholesterol [mm/dl] FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise] RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria] MaxHR: maximum heart rate achieved [Numeric value between 60 and 202] ExerciseAngina: exercise-induced angina [Y: Yes, N: No] Oldpeak: oldpeak = ST [Numeric value measured in depression] ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping] HeartDisease: output class [1: heart disease, 0: Normal] Source This dataset was created by combining different datasets already available independently but not combined before. In this dataset, 5 heart datasets are combined over 11 common features which makes it the largest heart disease dataset available so far for research purposes. The five datasets used for its curation are: Cleveland: 303 observations Hungarian: 294 observations Switzerland: 123 observations Long Beach VA: 200 observations Stalog (Heart) Data Set: 270 observations Total: 1190 observations Duplicated: 272 observations Final dataset: 918 observations Every dataset used can be found under the Index of heart disease datasets from UCI Machine Learning Repository on the following link: https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/ Citation fedesoriano. (September 2021). Heart Failure Prediction Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/heart-failure-prediction. Acknowledgements Creators: Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D. Donor: David W. Aha (aha '@' ics.uci.edu) (714) 856-8779

No description available

Repository purpose: A reproducible, privacy-first starter repo for building disease prediction models (diabetes, heart disease, etc.) using medical datasets. Includes data processing, baseline ML and DL models, evaluation, explainability, deployment examples (FastAPI + Streamlit), CI, and governance notes.

🧠 AI Health Disease Prediction System predicts multiple diseases like Heart, Liver, Kidney, Diabetes, and Brain Tumor using ML & DL models. 🩺💻 Enter basic health parameters to get instant AI-based predictions and insights. ⚡ User-friendly interface for self-checkup and research purposes. Expandable for future IoT and Transformer-based analytics.

This project uses Generative Adversarial Networks (GANs) to enhance diabetes prediction accuracy by generating synthetic patient data to address class imbalance. Multiple ML and DL algorithms—including Graph Neural Networks—were trained on the augmented dataset, achieving up to 97% accuracy, with a 40% improvement in model performance. Built using

No description available

No description available

No description available

Web Application-Diabetes prediction model using ML and DL models, frontend is made using streamlit

Hospital Readmission Prediction using ML & DL on the Diabetes 130-US hospitals dataset. Includes preprocessing, feature engineering, EDA, and models such as Logistic Regression, Random Forest, Gradient Boosting, Extra Trees, DNN, and 1D CNN. Achieves high accuracy and AUC for predicting 30-day readmissions.

The proposed diabetes disease prediction system consists of many steps which are perfectly linked to each other to get the desired results. The first step consists of splitting the used dataset into two subsets, training and testing data. Then, we applied two different categories (ML and DL methods) in order to carry out the training phase using the training samples with the best parameters. Eventually, the trained models will be able to predict the testing samples