TY  - CONF
ID  - digilib57587
UR  - https://ieeexplore.ieee.org/xpl/conhome/10006837/proceeding
A1  - Siregar, Maria Ulfah
A1  - Setiawan, Ichsan
A1  - Akmal, Najmunda Zia
A1  - Wardani, Dewi
A1  - Yunitasari, Yessi
A1  - Wijayanto, Ardhi
Y1  - 2023/01/13/
N2  - Heart failure is a serious long-term condition that
usually gets worse over time. On the other hand, some people
do not aware to check their heart health regularly. In this
study, the Random Forest will be optimized using the Genetic
Algorithm to obtain the best parameters and will be applied to
the heart failure dataset from Kaggle. We experimented with
two iterations for every nine combinations of the parameters.
We compared the results of optimized random forest, standalone
random forest, decision tree, and NaĻ?ve Bayes algorithms.
Our finding is that the optimized method is slightly better than
the other algorithms. The best F1-score is obtained at the second
iteration which is 0.90789 compared to 0.89404 obtained with
the sole random forest, 0.85034 obtained with the decision tree,
and 0.86195 obtained with Naive Bayes. The best recall value
is 0.91925 obtained in the first iteration, and in the second
iteration. The best recall is also obtained with the sole random
forest algorithm. The best precision value is 0.89937 which was
obtained in the first. By these results, the optimized random
forest algorithm could be used to result in reliable predictions
about heart failure.
KW  - decision tree; Naive Bayes; F1-score; recall; precision
TI  - Optimized Random Forest Classifier Based on
Genetic Algorithm for Heart Failure Prediction
M2  - Bali dan Online
AV  - public
T2  - 2022 Seventh International Conference on Informatics and Computing (ICIC)
ER  -