Comparison of C4.5 algorithm with naive Bayesian method in classification of Diabetes Mellitus (A case study at Hasanuddin University hospital Makassar)

Ente, Dewi Rahma; Arifin, Samsul; Andreza,; Thamrin, Sri Astuti

doi:10.1088/1742-6596/1341/9/092009

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…Using the Pima Indian Diabetes Dataset, the highest accuracy was achieved using the C4.5 algorithm, reaching 75.65%. Further confirmation of C4.5's superiority in classifying diabetes patients over other machine-learning methods was provided by Ente's work [6].…”

Section: Introductionmentioning

confidence: 88%

“…The alarming statistics necessitate various efforts to prevent the escalating number of diabetes patients, and one of these approaches involves utilizing medical record data [5]. In the realm of healthcare, a wealth of medical record data exists that could become invaluable if properly leveraged [6]. Thus, the effective utilization of medical record data is vital to gather information for curbing the prevalence of diabetes.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of Machine Learning Algorithm C4.5 in Classification of Patients With Type 2 Diabetes Mellitus

Purwaningrum,

Agustina

2024

BAREKENG: J. Math. & App.

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The neglect of a healthy lifestyle among the Indonesian population has led to an increased risk of diabetes mellitus, which currently affects 643 million people worldwide. Early and accurate diagnosis is crucial for preventing the progression of the disease. This study utilized the C4.5 machine learning algorithm to develop a model that can classify individuals as diabetic or non-diabetic based on factors associated with diabetes. The data used in this research consisted of medical records from patients with and without diabetes at Padang General Hospital. The model's performance evaluation resulted in a recall value of 91%. By promoting a healthy lifestyle and raising awareness about the importance of regular check-ups, the burden of diabetes can be reduced, and the overall health of the population can be improved.

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Section: Introductionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Implementation of Machine Learning Algorithm C4.5 in Classification of Patients With Type 2 Diabetes Mellitus

Purwaningrum,

Agustina

2024

BAREKENG: J. Math. & App.

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“…DeGregory et al (2018) telah membuktikan bahwa algoritma pembelajaran mesin menyediakan overview unik pada tahapan aplikasi analisis data pada obesitas. Rohman (2013) dan Ente et al (2019) juga telah menggunakan metode algoritma C4.5 untuk memprediksi penyakit jantung dan penyakit DM.…”

Section: Pendahuluanunclassified

Klasifikasi Faktor-Faktor Penyebab Penyakit Diabetes Melitus Di Rumah Sakit Unhas Menggunakan Algoritma C4.5

Ente

Thamrin

Arifin

et al. 2020

IJSA

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Diabetes mellitus (DM) is one of the chronic and deadly diseases that are widely observed in various countries today. This disease continues and is increasing to a very alarming stage. This study aims to identify and see the relationship between factors that influence DM disease. The method used in this research is C4.5 algorithm which is one of the algorithms used to make predictive classifications. Classification is one of the processes in data mining that aims to find patterns in relatively large data that use the representations in the form of decision trees. This method is applied to data from medical records of patients with DM in 2014-2018 taken from the Hasanuddin University Teaching Hospital. The results obtained indicate that there are four factors that influence the prediction of a patient's DM status namely; Fasting Blood Glucose (GDP), LDL Cholesterol, Triglycerides, and Body Weight.

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Determining the Eligibility of Providing Motorized Vehicle Loans by Using the Logistic Regression, Naive Bayes and Decission Tree (C4.5)

Rianto

Amrin

Rudianto

et al. 2020

J. Phys.: Conf. Ser.

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Evaluating in determining the eligibility of giving credit is very important. Errors in providing credit worthiness assessments can result a bad credit risk. The problem that often occurs is not the application of the system by financial parties but more on HR when making predictions about the determination of consumer credit worthiness. Research in the field of computers has been done to reduce credit risk resulting in losses to the company. In this research a comparison of Logistic Regression (LR), Naïve Bayes (NB) and Decision Tree (C4.5) algorithms is performed to predict the feasibility of granting credit. In order to produce a prediction of the feasibility of granting credit to new consumers, credit data used by the company is used. The data used in this study consists of 481 consumer records that have been classified as consumers with current credit and bad credit. After testing using the same dataset on the three algorithms by comparing the AUC and Confusion Matrix values, it was found that the appropriate algorithm to be applied to the credit worthiness dataset was Logistic Regression with an Area Under Curve (AUC) value of 0.972 and Accuracy or Confusion Matrix of 93.14%. As for the Decision Tree Algorithm (C4.5) from the test results, the AUC value is 0.926 and the Accuracy is 90.85% and the Algortima Naïve Bayes AUC value is 0.905 and the Accuracy is 82.75%.

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Comparison of C4.5 algorithm with naive Bayesian method in classification of Diabetes Mellitus (A case study at Hasanuddin University hospital Makassar)

Cited by 3 publications

References 1 publication

Implementation of Machine Learning Algorithm C4.5 in Classification of Patients With Type 2 Diabetes Mellitus

Implementation of Machine Learning Algorithm C4.5 in Classification of Patients With Type 2 Diabetes Mellitus

Klasifikasi Faktor-Faktor Penyebab Penyakit Diabetes Melitus Di Rumah Sakit Unhas Menggunakan Algoritma C4.5

Determining the Eligibility of Providing Motorized Vehicle Loans by Using the Logistic Regression, Naive Bayes and Decission Tree (C4.5)

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