End to End Heart Disease Prediction with Flask App using Machine Learning by Mahesh Huddar

Summary

This video demonstrates how to implement an end-to-end heart disease prediction project using machine learning algorithms. It covers building a web application with Flask where users input basic details like age, gender, and cholesterol levels to predict heart disease risk. The project uses various algorithms including Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbors. Viewers are guided through dataset preparation, model training, and integration into the Flask app. The video also provides the source code and resources for users to replicate and run the project.

Takeaways

• 😀 Cardiovascular diseases are the leading cause of death globally, responsible for 32% of all deaths in 2019, with heart attacks and strokes accounting for 85% of these fatalities.
• 😀 This project aims to build an end-to-end heart disease prediction system using machine learning algorithms and Flask to create a web application for predicting heart disease risk.
• 😀 The prediction system allows users to input personal details like age, gender, chest pain, and cholesterol level to estimate the likelihood of having heart disease.
• 😀 The project uses five machine learning algorithms: Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and K-Nearest Neighbors (KNN).
• 😀 The dataset consists of 14 columns, where the first 13 columns represent features and the last column is the target variable (indicating whether a person has heart disease).
• 😀 Data preprocessing steps include handling missing values, removing duplicates, and ensuring no correlation between features to prepare the data for model training.
• 😀 The machine learning models are trained on the dataset, and hyperparameters such as the number of neighbors for KNN and the kernel type for SVM are tuned to optimize performance.
• 😀 Model evaluation involves splitting the data into training and testing sets and measuring the accuracy of each model. The models are saved using the `pickle` library.
• 😀 The Flask application serves as the front-end interface, where users can input their health details, and the machine learning models predict the likelihood of heart disease based on these inputs.
• 😀 Once the Flask app is running, users can enter their information and receive predictions about their heart disease risk, along with detailed model outputs for each prediction.
• 😀 Users can generate and download a prediction report after submitting their data, allowing for easy tracking of results over time.

Q & A

What is the objective of the heart disease prediction project?

The objective is to predict the likelihood of heart disease in a patient using machine learning algorithms, based on various health parameters such as age, gender, cholesterol levels, and more.

Which machine learning algorithms were used in the project?

The project uses five machine learning algorithms: Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and K-Nearest Neighbors (KNN).

How is the dataset structured?

The dataset contains 14 columns, with the first 13 being feature columns (such as age, gender, chest pain type, cholesterol level), and the 14th column is the target variable indicating whether the patient has heart disease (1) or not (0).

What preprocessing steps were done on the dataset?

The data was checked for missing or duplicate values, and correlations between features were analyzed. Features were divided into input variables (X) and the target variable (Y), and the data was split into training and testing sets.

What is the significance of the K value in the K-Nearest Neighbors algorithm?

The K value in KNN determines how many neighbors to consider when making a prediction. In the project, the best K value was found to be 11, yielding 70% accuracy for heart disease prediction.

Which kernel was found to be the best for the Support Vector Machine (SVM) model?

The linear kernel for SVM was found to be the best, achieving 79% accuracy in predicting heart disease.

How did the Decision Tree algorithm perform in terms of accuracy?

The Decision Tree algorithm achieved the best accuracy (70%) when using all 13 features. Accuracy varied based on how many features were included, with one or two features yielding lower accuracy.

What was the optimal number of estimators for the Random Forest algorithm?

The optimal number of estimators for the Random Forest algorithm was 10, which provided the best accuracy of 82% in the project.

How were the models saved for future predictions?

The models were saved using Python's pickle library, with all trained models stored in a file called 'models.pkl'. This allows for future predictions without needing to retrain the models.

What is the role of the Flask web application in this project?

The Flask web application provides an interface for users to input their health data and receive predictions of their heart disease risk based on the machine learning models. The app also generates a report based on the predictions.