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Heart_Attack_Detection

About dataset

  • Age : Age of the patient

  • Sex : Sex of the patient

  • exang: exercise induced angina (1 = yes; 0 = no)

  • ca: number of major vessels (0-3)

  • cp : Chest Pain type chest pain type

    • Value 1: typical angina
    • Value 2: atypical angina
    • Value 3: non-anginal pain
    • Value 4: asymptomatic
  • trestbps : 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)

  • rest_ecg : resting electrocardiographic results

    • Value 0: normal
    • Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
    • Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
  • thalach : maximum heart rate achieved

  • target : 0= less chance of heart attack 1= more chance of heart attack

  • Data preparation was done thoroughly.
  • Did Hyper parameter tunning using GridSearchCV
  • Among GaussianNB, AdaBoostClassifier, DecisionTreeClassifier, KNeighborsClassifier, XGBoost, SVM, LogisticRegression, RandomForest, LogisticRegression, RandomForest performed best with score of 86%

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  • Jupyter Notebook 100.0%