Random Forest Algorithm to Investigate the Case of Acute Coronary Syndrome

Cynthia, Eka Pandu; Nazir, Alwis; Syafria, Fadhilah

doi:10.29207/resti.v5i2.3000

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…In Table 5, we present a comparison of our research with previous studies. In the study of [7]- [9] and this study, ACS cases were only under 32% of the total data, whereas in the [28] study ACS cases had 97% of the total data. The composition of the ACS data in these studies is imbalanced.…”

Section: Comparison With Previous Studiesmentioning

confidence: 51%

“…The study in [7] uses the artificial neural network (ANN) method with an F1 score of 0.849. Other researchers [8], [9] use a Decision Tree with an F1 score of 0.979 and the Random Forest algorithm with an accuracy of 83.45%. However, the results of previous studies were still not optimal because the dataset used to build the classification model was extremely imbalanced [10].…”

Section: Introductionmentioning

confidence: 99%

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Handling Imbalanced Data for Acute Coronary Syndrome Classification Based on Ensemble and K-Means SMOTE Method

Muzakki,

Prayogo,

Rizky A

2023

JOIV : Int. J. Inform. Visualization

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Acute Coronary Syndrome (ACS) is a disease that has a high mortality rate with a mortality percentage of 40% after 5 years from diagnosis. Despite the high mortality rate, the conventional process of overestimating ACS can be life-threatening. For this reason, several alternatives for prediagnosis have been investigated to reduce the detection of ACS intensively, one of which is by using a machine learning approach. The machine learning-based prediagnosis approach utilizes patient medical record data as input for making detection models. This approach can produce an optimal model when there is quite a lot of data and the labels have a fairly balanced comparison. However, in machine learning-based ACS detection studies, researchers often do not have balanced data between positive and negative labels that have the potential to cause overfitting. That problem occurs because obtaining additional data with specific labels is difficult. To solve the imbalanced problem in ACS detection, we generated synthetic ACS data using the K-Means SMOTE method. The synthesis data is used as training data to build an ensemble-based machine-learning model. In this study, we obtain an increase in the F1 score of more than 10% when compared to machine learning models that do not use the K-Means SMOTE as an oversampling process. In addition to the greater F1 score, the results obtained are relatively more resistant to overfitting because the data variations in the training set are more diverse.

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Section: Comparison With Previous Studiesmentioning

confidence: 51%

Section: Introductionmentioning

confidence: 99%

Handling Imbalanced Data for Acute Coronary Syndrome Classification Based on Ensemble and K-Means SMOTE Method

Muzakki,

Prayogo,

Rizky A

2023

JOIV : Int. J. Inform. Visualization

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“…Penggunaan algoritma Random Forest dalam menyelidiki permasalahan sindrom koroner kronis, dalam meninjau fungsi machine learning untuk penilaian menggunakan metode yang mengacu pada IBM dalam program python. Model pembagian data 70:30 mendapatkan hasil terbaik dengan akurasi 83,45%, recall 92,4%, serta presisi 85% [4].…”

Section: Pendahuluanunclassified

Penerapan Algoritma Random Forest Dan Cross Validation Untuk Prediksi Data Stunting

Fadellia Azzahra,

Suarna,

Arie Wijaya

2024

KOPERTIP

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Permasalahan yang seringkali terjadi pada anak dengan pertumbuhan yang tidak sesuai usianya merupakan kekurangan gizi kronis atau seringkali disebut stunting. Permasalahan anak stunting juga banyak terjadi di salah satu Kelurahan yang ada di Kota Cirebon yaitu di UPT Puskesmas XYZ, dari tingginya kasus stunting ini membuat Puskesmas menggelar kegiatan Posyandu rutin di setiap bulannya. Dari kegiatan Posyandu, Puskesmas akan memperoleh hasil laporan stunting dari pencatatan manual yang kemudian akan dilakukan penginputan data menggunakan Microsoft Excel. Pengidentifikasian dan penginputan data secara manual beresiko mengalami ketidaktepatan data serta status gizi yang menjadi indikator penentuan stunting yang beranekaragam dapat menghabiskan waktu lama. Untuk mengatasi permasalahan tersebut perlu melakukan penerapan data mining dengan algoritma Random Forest dan Cross Validation untuk prediksi data stunting. Pada riset yang akan dilakukan bertujuan untuk mengetahui cara kerja algoritma Random Forest dan Cross Validation serta untuk mendapatkan nilai akurasi, presisi, recall, dan RMSE terbaik. Hasil riset yang telah dilakukan dengan penerapan metode algoritma Random Forest memperoleh hasil pengujian confusion matrix dengan pembagian data 90:10 dalam memprediksi BB/TB menampilkan perolehan nilai yang sangat baik untuk akurasi 77.55%, recall 32.88%, presisi 21.97%, serta RMSE 0.402 dengan membuat model 40 pohon. Evaluasi dengan Cross Validation menghasilkan nilai terbaik pada 10-fold dengan mendapatkan nilai akurasi 78.33%, presisi 39.13%, recall 25.01%, serta RMSE 0.428 dengan membuat model 100 pohon.

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