Prediksi Penyakit Jantung Menggunakan Metode-Metode Machine Learning Berbasis Ensemble – Weighted Vote

Alhamad, Apriyanto; Azis, Azminuddin I. S.; Santoso, Budy; Taliki, Sunarto

doi:10.26418/jp.v5i3.37188

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Pada tahap ini, dilakukan optimasi pada program sebelum melangkah ke proses selanjutnya. Sehubungan pada pengolahan data ditemukan adanya nilai yang kosong, maka pada atribut kolom "ALB", "ALP", "ALT", "CHOL" dan "PROT", maka dapat diatasi dengan cara mengisi nilai rata-rata agar tidak terjadi bias [14] [15]. Selanjutnya, dilakukan pengecekan terhadap duplikasi data, namun tidak ditemukan adanya duplikasi data.…”

Section: Data Preprocessingunclassified

Analisis SMOTE Pada Klasifikasi Hepatitis C Berbasis Random Forest dan Naïve Bayes

Sharfina,

Ramadhan

2023

JOINTECS

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According to WHO, around 71 million people were infected with the Hepatitis C virus in 2019. However, only 49.7% of people are aware of Hepatitis C. Early prevention is essential to minimize the possibility of something terrible. To maximize the efforts of medical experts in minimizing the risk of transmission, a program was created that is capable of classifying Hepatitis C with an automatic detection system using a machine learning model. Random Forest was chosen because it can handle outlier and imbalance data so that it can produce high accuracy values and can identify important features. Naïve Bayes was chosen because of its simple algorithm, but capable of producing high-accuracy values. After testing both models, the confusion matrix formula calculates the prediction results. The test results show that applying the Random Forest model without SMOTE is 93%, and Naïve Bayes without SMOTE is 88%. Due to the data imbalance in the dataset, an oversampling technique is performed using the SMOTE method. The test results were obtained by applying the Random Forest model with a SMOTE of 98% and Naïve Bayes with a SMOTE of 89%.

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Section: Data Preprocessingunclassified

Analisis SMOTE Pada Klasifikasi Hepatitis C Berbasis Random Forest dan Naïve Bayes

Sharfina,

Ramadhan

2023

JOINTECS

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“…K-Nearest Neighbor (KNN) is a classification algorithm for learning data or objects based on the value of the knearest neighbor [16][18]. Where k serves as the distance or similarity between the data [19]. The algorithm works by finding the sample value closest to the sample input k and calculating the result based on the largest sample size of the input k [18].…”

Section: K-nearest Neighbormentioning

confidence: 99%

A Soft Voting Ensemble Classifier to Improve Survival Rate Predictions of Cardiovascular Heart Failure Patients

Munandar,

Maulana Baihaqi,

Nurhopipah

2023

Ilk. J. Ilm.

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Cardiovascular disease is one of the deadliest diseases, claiming around 17 million lives worldwide each year. According to data from the World Health Organization (WHO), more than four out of five deaths from cardiovascular disease are caused by heart attacks and strokes, and one-third of these deaths occur prematurely in people under the age of 70. Machine learning approaches can be used to detect the disease. This research aims to improve the prediction model of cardiovascular heart failure patient survival using C4.5, KNN, Logistic Regression algorithms, and the ensemble learning method of Voting Classifier. Based on the testing results, each model showed a significant increase in accuracy in the 70:30 ratio. Logistic Regression and C4.5 achieved the same accuracy, 89.47%, KNN obtained 91.23%, and Voting Classifier experienced a considerable improvement, reaching 94.74%. In testing with ratios of 90:10, 80:20, and 70:30, KNN demonstrated high accuracy but had significant overfitting, with a difference of 7-9% between training and testing accuracy scores in the 90:10 and 80:20 ratios. On the other hand, Voting Classifier showed stable performance in the 70:30 ratio, with an accuracy difference between training and testing scores below 1%. The conclusion of this research is that the Voting Classifier can assist the performance improvement of algorithms for classifying the survival expectancy of cardiovascular heart failure patients into 'Survived' or 'Deceased', compared to Logistic Regression, KNN, and C4.5.

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“…Penelitian M. Salman Pathan dkk menghasilkan akurasi 69 % menggunakan algoritma KNN dan 61% menggunakan algoritma Decision Tree [3]. Penelitian Apriyanto Alhamad, dkk menghasilkan akurasi 70,93 % menggunakan algoritma KNN, 81,26% menggunakan algoritma Decision Tree, 80,25 menggunakan algoritma SVM dan 81,76 menggunakan algoritma Naïve Bayes [4]. Namun hasil menunjukkan bahwa tingkat akurasi Decision tree dan KNN memiliki tingkat akurasi rendah dengan perbedaan yang tidak signifikan untuk itu diperlukannya adanya penanganan pada dataset yang memiliki atribut kontinyu menggunakan teknik discretization dan diperlukan adanya peningkatan akurasi untuk memberikan hasil keputusan yang terbaik.…”

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Penerapan Metode Discretization Dan Adaboost Untuk Meningkatkan Akurasi Algoritma Klasifikasi Dalam Memprediksi Penyakit Jantung

Majid

Miharja²

2022

IJUBI

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The mortality rate caused by heart disease can be reduced if there is an accurate diagnosis early on. Previous research in predicting heart disease with a high level of accuracy has been carried out but produces little accuracy in the Decision Tree C4.5 and K-Nearest Neighbor (KNN) algorithms. For this reason, it is necessary to increase accuracy in order to produce accurate information. The purpose of this research is to improve the accuracy of the Decision Tree C4.5 and K-Nearest Neighbor (KNN) classification algorithms using the heart disease dataset from kaggle.com by applying the discretization technique and the ensemble method, namely adaboost. The results of this study with a single algorithm produced an accuracy of 89.17% in the Decision Tree and 84.68% in KNN, while the Decision Tree used a discretization technique and adaboots of 99.81% and KNN used a discretization technique and adaboots of 92.88%. The results show an increase in the classification algorithm using discretization and adaboots techniques.

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Prediksi Penyakit Jantung Menggunakan Metode-Metode Machine Learning Berbasis Ensemble – Weighted Vote

Cited by 4 publications

References 13 publications

Analisis SMOTE Pada Klasifikasi Hepatitis C Berbasis Random Forest dan Naïve Bayes

Analisis SMOTE Pada Klasifikasi Hepatitis C Berbasis Random Forest dan Naïve Bayes

A Soft Voting Ensemble Classifier to Improve Survival Rate Predictions of Cardiovascular Heart Failure Patients

Penerapan Metode Discretization Dan Adaboost Untuk Meningkatkan Akurasi Algoritma Klasifikasi Dalam Memprediksi Penyakit Jantung

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