Abstract:
Heart failure (HF) is currently the leading cause of morbidity and mortality
worldwide. Identifying the risk of mortality at the early s
tages is crucial to
reducing the mortality rate. However, the traditional methods for exploring
the signs of mortality are difficult and time
-
consuming. Whereas,
m
achine
learning (ML) methods are superior in reducing HF’s mortality rate by
providing early
warnings. This study presents a novel ML classifier called
imperial boost
-
stacked
(IBS) that can serve as an effective early warning
system for predicting HF mortality. Initially, we performed an efficient data
balancing technique named
synthetic minority
oversampling technique with
edited nearest neighbors (
SMOTE
-
ENN
)
to mitigate the imbalance problem.
Next, two well
-
known feature selection techniques
, the
extra tree
(ET) and
information gain
(IG), are applied to reduce the data dimensions and select the
m
ost significant features. Following that, the prepared feature sets are trained
with our proposed IBS classifier. Simultaneously leveraging the advantages
of
boosting, stacking, and multiple
robust methods, it significantly correlates
with the intricate pa
tterns of clinical data of HF patients. Finally, the robust
outcomes of 92.75% accuracy over existing studies reveal that our proposed
study can effectively warn the HF mortality at early stages and reduce the
burden on the healthcare sector
