| The treatment of heart disease is costly and is not affordable for the common man. Hence, we can reduce this problem in some amount just by predicting heart disease before it becomes dangerous using the Heart Disease Prediction System Using Machine Learning and Data mining. If we can find out the heart disease problem in early stages then it will be very helpful for treatment. Machine Learning and Data Mining techniques are used for the construction of Heart Disease Prediction System. In the healthcare biomedical field, there is large use of healthcare data in the form of text, images, etc but, that data is hardly visited and is not mined. So, we can avoid this problem by introducing the Heart Disease Prediction System. This system is able to identify complex problems and is able to make intelligent medical decisions. |  |
There are twelve attributes in the dataset as follows:
- Age: age of the patient [years]
- Sex: sex of the patient [M: Male, F: Female]
- ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]
- RestingBP: resting blood pressure [mm Hg]
- Cholesterol: serum cholesterol [mm/dl]
- FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]
- RestingECG: resting electrocardiogram results
- MaxHR: maximum heart rate achieved [Numeric value between 60 and 202] = ExerciseAngina: exercise-induced angina [Y: Yes, N: No]
- Oldpeak: oldpeak = ST [Numeric value measured in depression]
- ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]
- HeartDisease: output class [1: heart disease, 0: Normal]
Clone the project
git clone https://github.com/Anushka-Nambiar/Heart-Disease-Prediction-SystemInstall the dependencies
pip install pandas
pip install matplotlib
pip install seabornRun the application
python manage.py runserver