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Heart Disease Prediction using Machine Learning with Flask App Project

77,22 zł 57,91 zł

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The following contents are sent to your mail immediately after the successful payment

  • Source Code

  • Flask App

  • Dataset

  • Project Report (Both PDF and Word files) 

  • Instructions to install the necessary Software and Libraries

  • Step-by-step instructions to execute the project

Project Description

According to World Health Organization statistics, cardiovascular disease is the leading cause of death in the world. CVDs were responsible for 32% of all global deaths in 2019, as estimated by the World Health Organization. Heart attacks and strokes were responsible for 85% of these deaths. Low- and middle-income countries account for more than three-quarters of all CVD deaths.

We have created a web application and a prediction model based on machine learning, which a patient can use to fill in basic details like age, gender, chest pain types, cholesterol level, etc. Based on these data, the model is able to predict heart disease. We have used various machine learning algorithms like Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and KNN for prediction. The user is also able to print the report to track the disease.

Steps in Implementing Heart Disease Prediction with Flask App using Machine Learning Project:-

The steps are broadly divided into the below steps. The sub-steps are also listed while we approach each of the steps.
  • Reading, understanding, and visualizing the data
  • Preparing the data for modeling
  • Building the model
  • Evaluate the model

We have used a total of 5 algorithms in our project

  1. Logistic Regression
  2. Support Vector Machine
  3. Decision Tree
  4. Random Forest, and
  5. KNN

Installation of software, libraries, and execution

1. Install the Anaconda Python Package
2. Open Anaconda Prompt and Move to the downloaded project directory (Heart Disease Prediction) using the cd command
Example:
>> cd Path_of_Project_Directory
3. Create the virtual environment using the below command
>>conda create -n hdp python==3.11.7
4. Activate the virtual environment using the command
>>conda activate hdp
5. Now install the required Libraries using the below command
>>pip install -r requirements.txt

Steps to train the model after Installation of required software and Libraries

1. Open Anaconda Prompt and Move to the downloaded project directory (Heart Disease Prediction) using the cd command
Example:
>> cd Path_of_Project_Directory
2. Activate the virtual environment using the command
>>conda activate hdp
Note: hdp is the environment created at the time of installing the software and Libraries
3. Next to train the model open the Jupyter Notebook using the below command
>>jupyter notebook
4. Open the Heart-Disease-Prediction.ipynb and run all cells
5. Once the training is completed the trained model models.pkl will be stored in the current working directory
Steps to run the Flask App after training the model 
1. Open Anaconda Prompt and Move to the downloaded project directory (Heart Disease Prediction) using the cd command
Example:
>> cd Path_of_Project_Directory
2. Activate the virtual environment using the command
>>conda activate hdp
Note: hdp is the environment created at the time of installing the software and Libraries
3. Run the Flask App using the below command
>>python app.py
 

Programming Languages and Libraries used

Language:  Python, Javascript,  CSS, HTML
Algorithms: Logistic Regression, SVM, Decision Tree, Random Forest, KNN
Framework: Flask
Tools: Anaconda, Jupyter notebook
Libraries: NumPy, Pandas, Matplotlib
 

Happy Learning

 

Still need help to set up and execute the project

  • Setup and modification are paid services based on requirements.