Heart disease prediction project using ML classification models

Introduction to the project

The rising incidence of heart disease is a major public health concern, underscoring the importance of early detection and intervention. Accurately and efficiently diagnosing heart disease is a challenging task, as it involves numerous risk factors, such as cholesterol levels, smoking habits, obesity, family history, blood pressure, and working conditions.

Thankfully, advances in machine learning algorithms have made it possible to develop predictive models that can help healthcare professionals identify individuals at risk of heart disease. By using regression models or the KNN method, machine learning algorithms can analyze large datasets of patient information and generate accurate predictions.

Developing these models can have a significant impact on public health, potentially saving tens of thousands of lives in the future. Being a part of such a critical endeavor is a humbling and inspiring experience, as it highlights the immense potential of machine learning to improve human health and well-being.

How others should get started

Install

This project requires Python and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• scikit-learn

You will also need to have software installed to run and execute a Jupyter Notebook.

If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included.

Code

Template code is provided in the HeartDiseacePrediction.ipynb notebook file. You will also be required to use the included HeartDiseacePrediction.ipynb Jupyter Notebook file and the heart.csv dataset file to complete your work.

Run

In a terminal or command window, navigate to the top-level project directory HeartDiseacePrediction/ (that contains this README) and run one of the following commands:

ipython notebook HeartDiseacePrediction.ipynb

or

jupyter notebook HeartDiseacePrediction.ipynb

or open with Juoyter Lab

jupyter lab

This will open the Jupyter Notebook software and project file in your browser.

About Dataset

This data set dates from 1988 and consists of four databases: Cleveland, Hungary, Switzerland, and Long Beach V. It contains 76 attributes, including the predicted attribute, but all published experiments refer to using a subset of 14 of them. The "target" field refers to the presence of heart disease in the patient. It is integer valued 0 = no disease and 1 = disease. Attribute Information: Description of the data

age - Age of the patient

sex - Sex of the patient

cp - Chest pain type ~ 0 = Typical Angina, 1 = Atypical Angina, 2 = Non-anginal Pain, 3 = Asymptomatic

trtbps - Resting blood pressure (in mm Hg)

chol - Cholestoral in mg/dl fetched via BMI sensor

fbs - (fasting blood sugar > 120 mg/dl) ~ 1 = True, 0 = False

restecg - Resting electrocardiographic results ~ 0 = Normal, 1 = ST-T wave normality, 2 = Left ventricular hypertrophy

thalach - Maximum heart rate achieved

oldpeak - Previous peak

slope - the slope of the peak exercise ST segment

caa - Number of major vessels

thal - Thalium Stress Test result ~ (0,3)

exng - Exercise induced angina ~ 1 = Yes, 0 = No

output - Target variable

All the output examples I have on each step you can see in HeartDiseasePrediction.ipynb Jupyter notebook file.

Machine Learning algorithms used:

Logistic Regression (Scikit-learn) Naive Bayes (Scikit-learn) K-Nearest Neighbours (Scikit-learn)

Have questions, contact me.

📧 Feel free to contact me @ [email protected]