Predicting mortality in heart failure patients using machine learning (PyCaret)

In this post, I’m going to develop machine learning models to predict death in heart failure patients.

Dataset

The description of the dataset can be here.

import pandas as pd

df = pd.read_csv('heart_failure_clinical_records_dataset.csv')
df.head()

	age	anaemia	creatinine_phosphokinase	diabetes	ejection_fraction	high_blood_pressure	platelets	serum_creatinine	serum_sodium	sex	smoking	time	DEATH_EVENT
0	75.0	0	582	0	20	1	265000.00	1.9	130	1	0	4	1
1	55.0	0	7861	0	38	0	263358.03	1.1	136	1	0	6	1
2	65.0	0	146	0	20	0	162000.00	1.3	129	1	1	7	1
3	50.0	1	111	0	20	0	210000.00	1.9	137	1	0	7	1
4	65.0	1	160	1	20	0	327000.00	2.7	116	0	0	8	1

df.shape
(299, 13)

Here I’m going to build classifiers using the machine learning library PyCaret.

from pycaret.classification import *

cls = setup(df, target='DEATH_EVENT', 
            categorical_features=['anaemia', 'diabetes', 'high_blood_pressure', 'sex', 'smoking'], 
            numeric_features=['age', 'creatinine_phosphokinase', 'ejection_fraction', 'platelets', 'serum_creatinine', 'serum_sodium'], 
            ignore_features=['time'])
compare_models()

	Model	Accuracy	AUC	Recall	Prec.	F1	Kappa	MCC	TT (Sec)
lr	Logistic Regression	0.7560	0.7323	0.4571	0.7388	0.5476	0.3967	0.4265	0.0050
ridge	Ridge Classifier	0.7371	0.0000	0.4429	0.6872	0.5214	0.3556	0.3816	0.0040
lda	Linear Discriminant Analysis	0.7371	0.7643	0.4429	0.6872	0.5214	0.3556	0.3816	0.0050
rf	Random Forest Classifier	0.7274	0.7681	0.5000	0.6433	0.5475	0.3598	0.3749	0.0330
catboost	CatBoost Classifier	0.7226	0.7690	0.4429	0.6788	0.5151	0.3332	0.3607	0.4300
nb	Naive Bayes	0.7033	0.6956	0.2857	0.6483	0.3909	0.2312	0.2686	0.0040
et	Extra Trees Classifier	0.6986	0.7374	0.3714	0.6421	0.4317	0.2536	0.2849	0.0310
ada	Ada Boost Classifier	0.6943	0.6952	0.4857	0.6104	0.5160	0.2997	0.3207	0.0090
xgboost	Extreme Gradient Boosting	0.6933	0.7191	0.4571	0.5673	0.4902	0.2798	0.2918	0.1980
lightgbm	Light Gradient Boosting Machine	0.6888	0.7279	0.4143	0.5753	0.4604	0.2570	0.2736	0.1200
gbc	Gradient Boosting Classifier	0.6798	0.6988	0.4857	0.5406	0.4998	0.2684	0.2774	0.0090
qda	Quadratic Discriminant Analysis	0.6795	0.6440	0.3286	0.5483	0.4027	0.2044	0.2206	0.0070
dt	Decision Tree Classifier	0.6557	0.6173	0.5000	0.5048	0.4900	0.2338	0.2423	0.0040
svm	SVM – Linear Kernel	0.6317	0.0000	0.1000	0.0333	0.0500	0.0000	0.0000	0.0040
knn	K Neighbors Classifier	0.6126	0.4562	0.1286	0.3083	0.1756	-0.0139	-0.0085	0.0060

Dataset

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