Comparison of Accuracy in Naïve Bayes and Random Forests in Classification of Liver Disease

Lubis, Ahmadi Irmansyah; Erdiansyah, Umri; Siregar, Rosma

doi:10.24114/cess.v7i1.28888

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Data normalization aims to remove any more invalid data before proceeding to the next step [13]. Normalize data lusing lthe lMinl-lMax lmethod lwith lthe lfollowing lformula [14]:…”

Section: Data Normalizationmentioning

confidence: 99%

Improved Decision Tree Accuracy (C4.5) with Attribute Reduction Using Forward Selection in Data Classification

Yohannes Cristanto,

Zamzami,

Fahmi

2024

Proceedings of the 11th International Applied Business and Engineering Conference, ABEC 2023, September 21st, 2023, Bengkalis,

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The main process in the formation of Decision Tree C4.5 is the separation of attributes. However, the attribute separation procedure in C4.5 still cannot optimize prediction accuracy in decision tree formation because unwanted features can lead to noisy data and less relevant features, which in turn can result in very large decision tree sizes (overfitting). As a result, the data becomes unbalanced and the classification accuracy of the Decision Tree C4.5 model becomes lower. To improve the accuracy of the classification process, attribute reduction is performed as a technique to simplify less relevant attributes. Therefore, forward selection is proposed as an attribute reduction method to produce mutually uncorrelated features, which are then used in Decision Tree C4.5 for classification. This study used datasets from the UCI Machine Learning Repository and Kaggle.com namely Diabetic Retinopathy Debrecen and South German Credit. Debrecen's Diabetic Retinopathy consists of 1,151 data records with 20 attributes, while South German Credit consists of 1000 data records with 20 attributes. Evaluation of classification performance is carried out based on the calculation of the Confusion Matrix. The test results showed that the proposed method was able to increase classification accuracy by 7.68%. Therefore, forward selection is considered an effective technique in reducing attributes and improving classification accuracy in Decision Tree C4.

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“…Data normalization aims to remove any more invalid data before proceeding to the next step [13]. Normalize data lusing lthe lMinl-lMax lmethod lwith lthe lfollowing lformula [14]:…”

Section: Data Normalizationmentioning

confidence: 99%

Improved Decision Tree Accuracy (C4.5) with Attribute Reduction Using Forward Selection in Data Classification

Yohannes Cristanto,

Zamzami,

Fahmi

2024

Proceedings of the 11th International Applied Business and Engineering Conference, ABEC 2023, September 21st, 2023, Bengkalis,

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“…Liver disease can be caused by various factors, such as congenital liver defects, viral or bacterial infections, alcohol addiction, smoking, unhealthy lifestyle choices, and other similar activities [4] [5]. One of the main functions affected is the liver's ability to neutralize toxins that enter the body.…”

Section: Introductionmentioning

confidence: 99%

Liver Disease Classification Analysis Using the Xgboost Method

Sitinjak,

Nababan

2023

JUSIKOM PRIMA

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Liver disease is a severe pathological condition that can cause liver inflammation due to viral infection, toxic agents, or bacterial invasion, interfering with normal liver function. The death rate from this disease reaches 1.2 million people annually in Southeast Asia and Africa. Liver disease can cause damage to the liver and negatively affect overall body function. To reduce disease progression, it is critical to facilitate early diagnosis, thereby enabling rapid initiation of treatment for affected individuals. Classification methods are widely used to make decisions based on new information from previous data processing through calculation algorithms. This study uses the XGBoost classification method to build a predictive model for liver disease. The results of this study confirm that the XGBoost model is a robust and efficient choice for liver disease classification based on patient data. The use of the XGBoost approach has proven its success in the category of liver disease with an accuracy of up to 95% and an accuracy balance of 95%, demonstrating the effectiveness and efficiency of this method in overcoming class imbalances in liver disease classification data. Keywords: Xgboost, Liver, Classification, Disease

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“…Beberapa peneletian terdahulu yang berkaitan dengan penelitian ini yaitu seperti penelitian yang dilakukan oleh rahman th 2020 melakukan penelitian mengenai penyakit liver menggunakan metode decision tree dan naive bayes yang mempunyai nilai optimal akurasi sebesar 70.29% (Rahman, 2020). Lubis, dkk juga melakukan penelitian sebelumnya dengan random forest dan naive bayes dalam memprediksi penyakit liver (Lubis, Erdiansyah dan Siregar, 2022). Berdasarkan penelitian yang telah dilakukan peneliti sebelumnya dengan metode Random Forest memperoleh hasil yaitu akurasi sebesar 70.60 % yang didapatkan hanya memiliki tapan preprocesing normalisasi data saja sehingga ini mencerminkan bahwa model klasifikasi yang didapat tidakalah bagus.…”

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Klasifikasi Data Mining Dengan Algoritma Machine Larning Untuk Prediksi Penyakit Liver

et al. 2023

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Liver merupakan organ tubuh manusia yang memiliki peranan sangat penting seperti mencerna, menyerap, membantu proses pencernaan makanan serta menghancurkan racun di dalam darah. Penyakit hati atau liver yang sudah akut sangat mempengaruhi fungsi-fungsi hati, penyakit hati dapat diketahui dari munculnya gejala klinis maupun fisik yang timbul pada pasien. Penelitian ini membahas tentang klasifikasi penyakit liver pada dataset ILPD yang diambil dari UCI Machine learning Repository menggunakan algoritma machine learning. Dataset terdiri dari 583 record data, 10 kriteria, dan 1 variable kelas berjenis multivariate. Penelitian ini menggunakan beberapa tahapan preprocessing yang dilakukan, diantaranya : Preprocessing Data Dan Eksplorasi Data, Penanganan missing value, feature selection, menerapkan feature correlation dan feature scaling, Analisis menggunakan Algoritma Machine learning. Berdasarkan hasil pengujian yang dilakukan dalam memperoleh nilai akurasi perhitungan klasifikasi menggunakan Algoritma Random Forest memiliki performa keakuratan yang diukur dengan akurasi sebesar 78,63% sehingga disimpulkan akurasi tersebut lebih unggul dari algoritma lainnya dalam klasifikasi penyakit liver.

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Comparison of Accuracy in Naïve Bayes and Random Forests in Classification of Liver Disease

Cited by 3 publications

References 7 publications

Improved Decision Tree Accuracy (C4.5) with Attribute Reduction Using Forward Selection in Data Classification

Improved Decision Tree Accuracy (C4.5) with Attribute Reduction Using Forward Selection in Data Classification

Liver Disease Classification Analysis Using the Xgboost Method

Klasifikasi Data Mining Dengan Algoritma Machine Larning Untuk Prediksi Penyakit Liver

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