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A comprehensive exploration of machine learning techniques and data science best practices applied to the UCI Heart Disease dataset. Focusing on data preprocessing, exploratory analysis, and predictive modelling to identify key factors in heart disease. Part of Big Data Management and Analytics (BDMA) program.
A machine learning application, deployed using Flask, is designed to identify the presence of heart disease in patients by analyzing various medical features.
CARDIOsetu is a web application designed to monitor individual heart health. It uses API integration to enable voice-to-text input for accessibility, making it easier for individuals with verbal and visual disabilities to interact with the app.
This project focuses on enhancing healthcare data security and privacy. We leveraged the Gaussian Differential Privacy (GDP) algorithm to protect individual patient information while enabling robust data analysis.
This is a machine learning project that uses various machine learning alogorithms to predict whether a patient is suffering from heart disease or not. Here I am using variour machine learning algorithms like Random Forest classifier, XGBClassifier, GaussianNB, Decision Tree Classifier, K-Nearest Neighbours and Logistic Regression.
Code of the Cardiovascular Risk Prediction Project, which is used to identify risk factors for cardiovascular disease related to coronary heart disease and stroke in adults.