kumar-laxmi / heart-disease-prediction-system Goto Github PK

Now days, Heart disease is the most common disease. But, unfortunately the treatment of heart disease is somewhat costly that is not affordable by common man. Hence, we can reduce this problem in some amount just by predicting heart disease before it becomes dangerous using Heart Disease Prediction System Using Machine Learning and Data mining. If we can find out heart disease problem in early stages then it becomes very helpful for treatment. Machine Learning and Data Mining techniques are used for the construction of Heart Disease Prediction System. In healthcare biomedical field, there is large use of heath care 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 Heart Disease Prediction System. This system will help us reduce the costs and to enhance the quality treatment of heart patients. This system can able to identify complex problems and can able to take intelligent medical decisions. The system can predict likelihood of patients of getting heart problems by their profiles such as blood pressure, age, sex, cholesterol and blood sugar. Also, the performance will be compared by calculation of confusion matrix. This can help to calculate accuracy, precision, and recall. The overall system provides high performance and better accuracy.

The health care industries collect huge amounts of data that contain some hidden information, which is useful for making effective decisions. For providing appropriate results and making effective decisions on data, some advanced data mining techniques are used. In this study, a Heart Disease Prediction System (HDPS) is developed using Naives Bayes and Decision Tree algorithms for predicting the risk level of heart disease. The system uses 13 medical parameters such as age, sex, blood pressure, cholesterol, and obesity for prediction. The HDPS predicts the likelihood of patients getting heart disease. It enables significant knowledge. E.g. Relationships between medical factors related to heart disease and patterns, to be established. We have employed the multilayer perceptron neural network with back propagation as the training algorithm. The obtained results have illustrated that the designed diagnostic system can effectively predict the risk level of heart diseases.

To predict heart disease according to input parameter values provided by user and dataset stored in database.

The main objective of this project is to develop a heart disease prediction system. The system can discover and extract hidden knowledge associated with diseases from a historical heart data set Heart disease prediction system aims to exploit data mining techniques on medical data set to assist in the prediction of the heart diseases.

The project has a wide scope, as it is not intended to a particular organization. This project is going to develop generic software, which can be applied by any businesses organization. Moreover it provides facility to its users. Also the software is going to provide a huge amount of summary data.

• Patient Login:- Patient Login to the system using his ID and Password.
• Patient Registration:_ If Patient is a new user he will enter his personal details and he will user Id and password through which he can login to the system.
• My Details:- Patient can view his personal details.
• Disease Prediction:- - Patient will specify the input parameter values. System will take input values and predict the disease based on the input data values specified by the patient and system will also suggest doctors based on the locality
• Search Doctor:- Patient can search for doctor by specifying name, address or type.
• Feedback:- Patient will give feedback this will be reported to the admin
• Doctor Login:- Doctor will access the system using his User ID and Password.
• Patient Details:- Doctor can view patient’s personal details.
• Notification:- Admin and doctor will get notification how many people had accessed the system and what all are the diseases predicted by the system.
• Admin Login:- Admin can login to the system using his ID and Password.
• Add Doctor:- Admin can add new doctor details into the database.
• Add Dataset:- Admin can add dataset file in database.
• View Doctor:- Admin can view various Doctors along with their personal details.
• View Disease:- Admin can view various diseases details stored in database.
• View Patient:- Admin can view various patient details that had accessed the system.
• View Feedback:- Admin can view feedback provided by various users.

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• FrameWork:

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• Machine-Learning Algorithms:

  • GRADIENT BOOSTING ALGORITHM
  • LOGISTIC REGRESSION

• ML/DL:

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• Database:
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• Data-Set for training:

  • Click here for DATA-SET

• IDE:

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• OS used for testing:

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Clone the project

  git clone https://github.com/Kumar-laxmi/Heart-Disease-Prediction-System

Go to the project directory

  cd Heart-Disease-Prediction-System

Start the server

  python manage.py runserver

def prdict_heart_disease(list_data):
    csv_file = Admin_Helath_CSV.objects.get(id=1)
    df = pd.read_csv(csv_file.csv_file)

    X = df[['age','sex','cp','trestbps','chol','fbs','restecg','thalach','exang','oldpeak','slope','ca','thal']]
    y = df['target']
    X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, random_state=0)
    nn_model = GradientBoostingClassifier(n_estimators=100,learning_rate=1.0,max_depth=1, random_state=0)
    nn_model.fit(X_train, y_train)
    pred = nn_model.predict([list_data])
    print("Neural Network Accuracy: {:.2f}%".format(nn_model.score(X_test, y_test) * 100))
    print("Prdicted Value is : ", format(pred))
    dataframe = str(df.head())
    return (nn_model.score(X_test, y_test) * 100),(pred)

For demo video Click Here

When the application is runned then, a Welcome Page pops-up

Admin Dash-board:

Entering Heart Details to check our Health:

Since these details are stored in the Data-base, so we can also retrieve past results:

To view our own details:

If a user doesn't understand how to use the application then he can:

To view registered Doctor information: