autoprognosis.plugins.prediction.risk_estimation.benchmarks.cvd.framingham.model module

class FraminghamModel

Bases: object

fit(*args: Any, **kwargs: Any) → FraminghamModel

predict(df: DataFrame, times: list = []) → DataFrame

score(X: DataFrame, Y: DataFrame) → float

inference(sex: str, age: int, total_cholesterol: float, hdl_cholesterol: float, systolic_blood_pressure: int, smoker: bool, blood_pressure_med_treatment: bool) → float

Requires: sex - “M” or “F” string age - int total_cholesterol - int hdl_cholesterol - int systolic_blood_pressure - int smoker - True or False. blood_pressure_med_treatment - True or False.

mmolL_to_mgdl(val: float) → float