Classification Of Coronary Artery Disease
Classification of patient with Coronary Artery Disease
@kaggle.saeedeheydarian_classification_of_coronary_artery_disease
Classification of patient with Coronary Artery Disease
@kaggle.saeedeheydarian_classification_of_coronary_artery_disease
The Z-Alizadeh Sani dataset is one of the most common datasets used in machine learning for automatic CAD detection. This dataset contains 303 samples (216 CAD patients and 87 normal) with 55 features. The main features of this dataset are four categories: (1) Demographic, (2) symptoms and examination, (3) electrocardiogram, and (4) laboratory and echo features.
Features of Z-Alizadeh Sani dataset
Demographic:
Symptom and examination :
ECG:
Laboratory and echo:
CREATE TABLE cad (
"age" BIGINT,
"weight" BIGINT,
"length" BIGINT,
"sex" VARCHAR,
"bmi" DOUBLE,
"dm" BIGINT,
"htn" BIGINT,
"current_smoker" BIGINT,
"ex_smoker" BIGINT,
"fh" BIGINT,
"obesity" VARCHAR,
"crf" VARCHAR,
"cva" VARCHAR,
"airway_disease" VARCHAR,
"thyroid_disease" VARCHAR,
"chf" VARCHAR,
"dlp" VARCHAR,
"bp" BIGINT,
"pr" BIGINT,
"edema" BIGINT,
"weak_peripheral_pulse" VARCHAR,
"lung_rales" VARCHAR,
"systolic_murmur" VARCHAR,
"diastolic_murmur" VARCHAR,
"typical_chest_pain" BIGINT,
"dyspnea" VARCHAR,
"function_class" BIGINT,
"atypical" VARCHAR,
"nonanginal" VARCHAR,
"exertional_cp" VARCHAR,
"lowth_ang" VARCHAR,
"q_wave" BIGINT,
"st_elevation" BIGINT,
"st_depression" BIGINT,
"tinversion" BIGINT,
"lvh" VARCHAR,
"poor_r_progression" VARCHAR,
"fbs" BIGINT,
"cr" DOUBLE,
"tg" BIGINT,
"ldl" BIGINT,
"hdl" DOUBLE,
"bun" BIGINT,
"esr" BIGINT,
"hb" DOUBLE,
"k" DOUBLE,
"na" BIGINT,
"wbc" BIGINT,
"lymph" BIGINT,
"neut" BIGINT,
"plt" BIGINT,
"ef_tte" BIGINT,
"region_rwma" BIGINT,
"vhd" VARCHAR,
"cath" VARCHAR
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