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This project involves training of Machine Learning models to predict the Heart Failure for Heart Disease event. In this KNN gives a high Accuracy of 89%.

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KIET7UKE/Heart-Failure-Prediction-ML

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Heart-Failure-Prediction

Dataset contains 11 clinical features for predicting heart disease events.

This project helps to predict whether the patient has Heart Disease or not. This prediction is made using the clinical data of patients.

Dataset Discription

Attribute Description
Age Age of a patient [years]
Sex Gender of the patient [M: Male, F: Female]
ChestPain Chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]
RestingBP Blood pressure in Hg (Normal blood pressure - 120/80 Hg)
Cholesterol Serum cholestrol level in blood (Normal cholesterol level below for adults 200mg/dL)
FastingBS Fasting Blood Sugar (Normal less than 100mg/dL for non diabetes for diabetes 100-125mg/dL)
RestingECG Resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria]
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]

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This project involves training of Machine Learning models to predict the Heart Failure for Heart Disease event. In this KNN gives a high Accuracy of 89%.

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