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Analysed the performance of different Machine Learning algorithms on Coronary heart disease dataset acquired from Kaggle. Performed EDA, Data cleansing, data pre processing and feature correlation, feature selection. Implemented Logistic regression with 10 fold cross validation, Logistic regression with GridSearchCV, Random Forest, RNN and MLP
This is a Medical Prediction App which can be used to predict the current disease state of any human from any part of the world. This includes 3 main type of diseases - Covid-19, Diabetes, Heart Disease. Additionally it has a Medical Suggestions section which has some tips and guidelines for the ones affected by any of the disease
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.
This project develops a machine learning model to predict heart disease risk based on symptoms and medical history. The model achieved the best accuracy with Logistic Regression, as it works well for binary classification problems.
The Heart Disease Prediction Model uses Logistic Regression to predict heart disease risk from user-inputted medical data through a Flask web app. Users enter details like age and blood pressure to get predictions, with model persistence handled by pickle. Future enhancements include UI improvements and additional machine learning models.
Agent Based Software Engineering Semester Project in python: Heart Disease Prediction . Complete User Interface along MYSQL database connection to store data .
This repository presents a machine learning project aimed at building a predictive model for heart disease. To improve the model's ability to classify patients who are borderline cases, we employ Synthetic Minority Over-sampling Technique (SMOTE).