heart-disease-ml-api

This is a machine learning project built in Python/Flask. It uses SkLearn's random forest classifier to predict the probability of heart disease based on medical history input. The API is hosted on Heroku. I removed cholesterol from the calculation do to too many empty entries for the feild.

Source

The referenced dataset comes from: https://www.kaggle.com/fedesoriano/heart-failure-prediction

How to use

Send a POST request to the following link: https://heart-disease-ml-api.herokuapp.com/prediction The body of your request must the following data in JSON format:

{
    "Age": 60,
    "Sex": M,
    "ChestPainType":TA,
    "RestingBP": 190,
    "FastingBS": 1,
    "RestingECG": LVH,
    "MaxHR": 100,
    "ExerciseAngina":Y,
    "Oldpeak":2.0,
    "ST_Slope":Up
}

• Age: age of the patient [years]
• Sex: sex of the patient [F: Female, M: Male]
• ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]
• RestingBP: resting blood pressure (systolic) [mm Hg]
• FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]
• 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]