🫀 We would be using machine learning models to predict the chances of a patient suffering from a heart disease using various features such as cholesterol levels and chest pain type. We would be just considering a sample dataset just to get an understanding of the various machine learning models that could be put to action and learn their implementation. We would also be considering various classification metrics just to compare how well the models did on the test data.

A machine learning tool that predicts the likelihood of cancer and heart disease using advanced classification models. The repository includes features for data preprocessing, hyperparameter tuning, batch predictions, and model evaluation, aimed at enhancing early diagnosis and health insights.

A variety of conditions that affect your heart are referred to as heart disease. According to World Health Organization reports, cardiovascular diseases are now the leading cause of death worldwide, with 17.9 million deaths per year. Artificial intelligence and machine learning are now widely acknowledged to play an important role in the medical field, where they are used to diagnose diseases, classify or forecast outcomes using a variety of machine learning and deep learning models. Machine learning algorithms can quickly adapt to a thorough analysis of genetic data. For accurate estimation, medical records can be changed and studied more thoroughly, and better models can be identified for accurate prediction. Using a different algorithm, several researchers have reported on the prediction of heart problems.The aim of this study is to diagnose heart disease using machine learning algorithms. Machine Learning can help predict the presence or absence of locomotor disorders, heart diseases, and other conditions. Artificial intelligence (AI) has the potential to solve this problem right now. To improve the classification accuracy of a heart disease data set, we propose combining KNN, logistics regression, SVM, Random Forest algorithm, and decision tree algorithm. The proposed approach was applied to the dataset, which included first a thorough analysis of the data, followed by the use of various machine learning algorithms, including linear model selection and Logistic Regression. KNeighborsClassifier was used to focus on neighbour selection, followed by a tree-based technique like DecisionTreeClassifier, and finally a very popular and most popular ensemble method RandomForestClassifier. Support Vector Machine was also used to check and handle the data's high dimensionality.

This project uses machine learning technique(Logistic Regression) to predict the likelihood of heart disease. A classification model is trained on real-world health data to assist in early detection and prevention of heart-related conditions.

Predicting whether or not a person has heart disease using LogisticRegression Machine Learning Classification Model

Machine learning project that predicts the risk of heart disease using multiple classification models and deploys the best model (KNN) through a Streamlit web application.

Supervised ML - Classification Using Python this project demonstrates the effectiveness of machine learning techniques in predicting cardiovascular risk using the Framingham Heart Study dataset. The developed machine learning model can be used by healthcare professionals to identify individuals at high risk of cardiovascular disease .

Machine-learning project that predicts the risk of heart disease using patient data such as age, blood pressure, and cholesterol. Built in Python with Jupyter Notebook, using data cleaning, EDA, and classification models to achieve accurate early-detection insights.

Heart-Disease-Prediction-using-Machine-Learning Thus preventing Heart diseases has become more than necessary. Good data-driven systems for predicting heart diseases can improve the entire research and prevention process, making sure that more people can live healthy lives. This is where Machine Learning comes into play. Machine Learning helps in predicting the Heart diseases, and the predictions made are quite accurate. The project involved analysis of the heart disease patient dataset with proper data processing. Then, different models were trained and and predictions are made with different algorithms KNN, Decision Tree, Random Forest,SVM,Logistic Regression etc This is the jupyter notebook code and dataset I've used for my Kaggle kernel 'Binary Classification with Sklearn and Keras' I've used a variety of Machine Learning algorithms, implemented in Python, to predict the presence of heart disease in a patient. This is a classification problem, with input features as a variety of parameters, and the target variable as a binary variable, predicting whether heart disease is present or not. Machine Learning algorithms used: Logistic Regression (Scikit-learn) Naive Bayes (Scikit-learn) Support Vector Machine (Linear) (Scikit-learn) K-Nearest Neighbours (Scikit-learn) Decision Tree (Scikit-learn) Random Forest (Scikit-learn) XGBoost (Scikit-learn) Artificial Neural Network with 1 Hidden layer (Keras) Accuracy achieved: 95% (Random Forest) Dataset used: https://www.kaggle.com/ronitf/heart-disease-uci

Heart-Disease-Prediction-using-Machine-Learning Thus preventing Heart diseases has become more than necessary. Good data-driven systems for predicting heart diseases can improve the entire research and prevention process, making sure that more people can live healthy lives. This is where Machine Learning comes into play. Machine Learning helps in predicting the Heart diseases, and the predictions made are quite accurate. The project involved analysis of the heart disease patient dataset with proper data processing. Then, different models were trained and and predictions are made with different algorithms KNN, Decision Tree, Random Forest,SVM,Logistic Regression etc This is the jupyter notebook code and dataset I've used for my Kaggle kernel 'Binary Classification with Sklearn and Keras' I've used a variety of Machine Learning algorithms, implemented in Python, to predict the presence of heart disease in a patient. This is a classification problem, with input features as a variety of parameters, and the target variable as a binary variable, predicting whether heart disease is present or not. Machine Learning algorithms used: Logistic Regression (Scikit-learn) Naive Bayes (Scikit-learn) Support Vector Machine (Linear) (Scikit-learn) K-Nearest Neighbours (Scikit-learn) Decision Tree (Scikit-learn) Random Forest (Scikit-learn) XGBoost (Scikit-learn) Artificial Neural Network with 1 Hidden layer (Keras) Accuracy achieved: 95% (Random Forest) Dataset used: https://www.kaggle.com/ronitf/heart-disease-uci

This project predicts Coronary Heart Disease (CHD) using machine learning classification and regression models. It includes complete exploratory data analysis (EDA), data preprocessing, feature engineering, and evaluates multiple ML algorithms such as Logistic Regression, Naïve Bayes, Decision Trees, Random Forest and etc.

Thus preventing Heart diseases has become more than necessary. Good data-driven systems for predicting heart diseases can improve the entire research and prevention process, making sure that more people can live healthy lives. This is where Machine Learning comes into play. Machine Learning helps in predicting the Heart diseases, and the predictions made are quite accurate. The project involved analysis of the heart disease patient dataset with proper data processing. Then, different models were trained and and predictions are made with different algorithms KNN, Decision Tree, Random Forest,SVM,Logistic Regression etc This is the jupyter notebook code and dataset I've used for my Kaggle kernel 'Binary Classification with Sklearn and Keras' I've used a variety of Machine Learning algorithms, implemented in Python, to predict the presence of heart disease in a patient. This is a classification problem, with input features as a variety of parameters, and the target variable as a binary variable, predicting whether heart disease is present or not. Machine Learning algorithms used: Logistic Regression (Scikit-learn) Naive Bayes (Scikit-learn) Support Vector Machine (Linear) (Scikit-learn) K-Nearest Neighbours (Scikit-learn) Decision Tree (Scikit-learn) Random Forest (Scikit-learn) XGBoost (Scikit-learn) Artificial Neural Network with 1 Hidden layer (Keras) Accuracy achieved: 95% (Random Forest)

Healthcare expenditures are overwhelming national and corporate budgets due to asymptomatic diseases including cardiovascular diseases. Therefore, there is an urgent need for early detection and treatment of such diseases. Machine learning is one of the trending technologies which used in many spheres around the world including healthcare industry for predicting diseases. The aim of this study was to identify the most significant predictors of heart diseases and predicting the overall risks by using logistic regression. Thus, binary logistic model which is one of the classification algorithms in machine learning was used in this study to identify the predicators. Further, data analysis was carried out in Python using JupyterLab in order to validate the logistic regression.

Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).

Healthcare is an important task to be performed in human life. CVD is the category to which the diseases affecting the heart and blood vessels are included. The methods used before for forecasting the CVD helped in reducing the risks in high risk patients. The health care industry contains lots of medical data, so machine learning algorithms are made use to efficiently predict the heart diseases decisions. We classify the attributes for prediction and decision making at different levels. The performance is based on classification, accuracy, sensitivity. This project proposes a Machine learning model to predict whether a person has a chance of suffering from any CVD or not and to provide an awareness and diagnosis. This is done by applying algorithms of machine learning for classification and prediction. Some of the algorithms we are using here are-K-NN (Nearest Neighbor), Support Vector Machine(SVM), Random forest.

The prediction of heart disease is considered one of the most important topics in health domain. With the machine learning algorithms and having large amounts of data, it is possible to extrapolate information that can help doctors make more accurate predictions. Prediction of CHD is a much complex challenge considering the level of expertise and knowledge required for accurate result. According to a survey by WHO, medical professionals can correctly predict heart disease with only 67% accuracy. In this project , a number of independent variables such as sex, age, cigsPerDay, totChol, sysBP and glucose will be used along with a dependent variable (TenYearCHD class) during the training phase to build a classification model. The classification goal is to predict whether the patient has 10-year risk of future Coronary Heart Disease (CHD) or not.

## Project Demo URL https://diabetiesproject.azurewebsites.net •This repository consists of files required to deploy a **Web App** created with **Flask on Microsoft Azure**.# diabetes_predictor The project helps the user to identify whether someone is suffering from diabetes by simply inputting certain values like BMI, Glucose level, Blood pressure etc. with the help of a Kaggle database. By using the statistical data about how certain aspects like BMI, Glucose level, Insulin level, age etc. impact if an individual is prone to diabetes or not, the project will be able to tell the user if the person has diabetes or not by entering those values. So in a way the project will help in monitoring the likelihood of someone developing diabetes. The project can be extended to include other diseases prediction which I will incorporate later down the road. ### Problem Statement/Oppurtunity: Diabetes is an illness caused because of high glucose level in a human body. Diabetes should not be ignored if it is untreated then Diabetes may cause some major issues in a person like: heart related problems, kidney problem, blood pressure, eye damage and it can also affects other organs of human body. Diabetes can be controlled if it is predicted earlier. To achieve this goal this project work we will do early prediction of Diabetes in a human body or a patient for a higher accuracy through applying, Various Machine Learning Techniques. ## Project Discription: Machine learning techniques Provide better result for prediction by con- structing models from datasets collected from patients. In this work we will use Machine Learning Classification and ensemble techniques on a dataset to predict diabetes. Which are K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Gradient Boosting (GB) and Random Forest (RF). The accuracy is different for every model when compared to other models. The Project work gives the accurate or higher accuracy model shows that the model is capa- ble of predicting diabetes effectively. Our Result shows that Random Forest achieved higher accuracy compared to other machine learning techniques. The is a Microsoft Azure Web App project that helps the user to identify whether someone is suffering from diabetes by simply inputting certain values like BMI, Glucose level, Blood pressure etc. with the help of Kaggle database. ## Primary Ajure Technology: **Azure Web Apps , AI+Machine Learning, Computer Vision, Static Web Apps, Web Apps** ## Conclusion: Hence we succesfully predict that any person having diabities or not. Thank You # f r t p r o j e c t # f r t p r o j e c t

Repository Name: Predicting Heart Disease with Machine Learning Description: This repository contains a machine learning project focused on predicting heart disease using classification algorithms. It leverages data preprocessing, feature selection, and model evaluation techniques to build accurate and reliable models.

Machine learning project to predict heart disease using clinical features and classification models.

Machine learning project for predicting heart disease risk using classification models and healthcare data analysis.

Predict the presence of heart disease in human beings using machine learning classification models

End-to-end machine learning project for predicting heart disease using classification models and cross-validation.

End-to-end machine learning project predicting heart disease using classification models with EDA, model comparison, and evaluation.

Machine learning project predicting heart disease risk using clinical patient data with data preprocessing, exploratory analysis, and classification models.

Using supervised Machine Learning algorithms to build models for classification of Heart Diseases among patients and predicting the risk of getting a Heart Disease in the future.

CLASSIFICATION MODEL: Heart Disease Prediction Model This project builds a machine learning model to predict the presence of heart disease in patients using 14 key medical records.

A Multifaceted Approach to Predictive Modeling: BMI Prediction, Heart Disease Classification, and Celestial Object Recognition using Machine Learning

Machine learning project that predicts heart disease using classification models like Random Forest and Logistic Regression. Includes EDA, hyperparameter tuning, and model evaluation.

HeartHealthAI: A machine learning project for predicting heart disease risks using data analysis, classification models, and Explainable AI techniques. Early detection made actionable and transparent.

A simple Streamlit-based machine learning app that predicts the risk of heart disease using clinical input parameters and a trained classification model