The Implementation of Z-Score Normalization and Boosting Techniques to Increase Accuracy of C4.5 Algorithm in Diagnosing Chronic Kidney Disease

Prihanditya, Hestu Aji; Alamsyah, Alamsyah

doi:10.52465/joscex.v1i1.8

Cited by 15 publications

(7 citation statements)

References 7 publications

(7 reference statements)

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“…This method is also called standard observational assessment. It indicates the location of the initial estimate in terms of the mean value when measured in units of standard deviation [15]; -insulating forest. This mathematical model uses binary decision trees.…”

Section: Modules Development and Their Integration Into The Stratific...mentioning

confidence: 99%

Application of a data stratification approach in computer medical monitoring systems

Donets,

Shevchenko,

Holikov

et al. 2024

EEJET

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The research object is the processes occurring in the data stratification subsystem in the medical monitoring computer system, which is part of the decision support system. Such a subsystem aims to solve data analysis and processing problems in the medical monitoring system. Among them, the problems of anomaly detection, data marking, state determination, selection of the most informative variables, and justification of decision-making are selected for solving. The paper proposes the structure and implementation of the data stratification subsystem in the decision support system. The subsystem contains modules for anomaly detection and an autoencoder, a clustering module using an advanced multi-agent clustering method, and a state detection module with a modified neural network training procedure. Modules of the stratification subsystem have been tested using diabetes monitoring data. The results showed that the clustering module provides 25.7 % lower accuracy than the achieved neural network. The accuracy difference is explained by the complexity of the data and the lack of adaptability of the proposed method to solving such problems. It is shown that the method of determining the overall informativeness of variables covers 90 % informativeness with 10 variables, comparable to the variability data. In general, the flexible nature of the proposed stratification subsystem allows for solving the problems. The proposed stratification subsystem offers a robust solution for improving treatment strategies and decision-making in computer medical monitoring systems. Its versatility allows it to be used in any system where decision support is needed, providing valuable information about informative variables and decision-making features for clinicians and researchers

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Section: Modules Development and Their Integration Into The Stratific...mentioning

confidence: 99%

Application of a data stratification approach in computer medical monitoring systems

Donets,

Shevchenko,

Holikov

et al. 2024

EEJET

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show abstract

“…Normalization was performed to minimize the effect of size change among the analyzed epochs [60]. By applying the Z-score normalized method [61], signals whose amplitude is normalized and frequency components were formed in the desired band range were prepared for feature extraction.…”

Section: Preprocessingmentioning

confidence: 99%

Comparison of EEG and EOG signals in classification of sleep stages

Melek

2023

Pamukkale J Eng Sci

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The value of sleep, which is the most significant part of life, increases with the emergence of health problems caused by insomnia. To solve this problem, it is extremely important to interpret the different signal patterns that occur during sleep stages. In order to achieve this goal, systems are created that provide automatic scoring of sleep stages. In sleep scoring, valuable information about sleep is obtained by considering the electrophysiological signals of the sleeper. The ISRUC-Sleep dataset, which was presented as open access to researchers working in the field of sleep, was used in the study. The main goal of the study is to investigate the effect of electroencephalography (EEG) and electrooculography (EOG) biosignals in the classification of sleep stages. The analysis was carried out by considering the third group of the data set, which defines three different groups belonging to the ISRUC platform. The 10 participants of subgrup_3 in the dataset were considered. By extracting effective features and applying different classification methods, it was investigated which one of the EEG or EOG signals was better in the classification of stages. In terms of performance evaluation of the classification methods used, the new Roza metric presented in our previous study was applied. It has been proven that EEG signals are more successful than EOG in the classification of sleep stages, thanks to the Welch feature extraction method and the ensemble of bagged tree classification technique. These sleep stages were classified by using EEG signals with a success rate of 77.7%. Yaşamın en önemli parçası olan uykunun değeri, uykusuzluğun neden olduğu sağlık sorunlarının ortaya çıkmasıyla birlikte artmaktadır. Bu sorunu çözmek için uyku evrelerinde ortaya çıkan farklı sinyal kalıplarını yorumlamak son derece önemlidir. Bu amaca ulaşmak için uyku evrelerinin otomatik olarak puanlanmasını sağlayan sistemler oluşturulur. Uyku puanlamasında uyuyan kişinin elektrofizyolojik sinyalleri dikkate alınarak uyku hakkında değerli bilgiler elde edilir. Çalışmada uyku alanında çalışan araştırmacılara açık erişim olarak sunulan ISRUC-Sleep veri seti kullanılmıştır. Çalışmanın temel amacı, uyku evrelerinin sınıflandırılmasında elektroensefalografi (EEG) ve elektrookülografi (EOG) biyosinyallerinin etkisini araştırmaktır. Analiz, ISRUC platformuna ait üç farklı grubu tanımlayan veri setinin üçüncü grubu dikkate alınarak gerçekleştirilmiştir. Veri setindeki alt grup_3'ün 10 katılımcısı dikkate alınmıştır. Etkili öznitelikler çıkarılarak ve farklı sınıflandırma yöntemleri uygulanarak aşamaların sınıflandırılmasında EEG veya EOG sinyallerinden hangisinin daha iyi olduğu araştırılmıştır. Kullanılan sınıflandırma yöntemlerinin performans değerlendirmesi açısından önceki çalışmamızda sunulan yeni Roza metriği uygulanmıştır. Welch öznitelik çıkarma yöntemi ve toplu ağaç sınıflandırma tekniği sayesinde uyku evrelerinin sınıflandırılmasında EEG sinyallerinin EOG'dan daha başarılı olduğu kanıtlanmıştır. Bu uyku evreleri EEG sinyallerini kullanara...

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“…In ID3, the induction decision tree can only be performed on categorical features (nominal/ ordinal), while numeric types (internal / ratio) cannot be used. It is the most influential decision tree algorithm at present [16]. Data Mining makes it easier for educational institutions to identify based on what factors influence students to get a good Grade Point Average in class [17].…”

Section: Introductionmentioning

confidence: 99%

Prediction of PTIK students' study success in the first year using the c4.5 algorithm

Astuti,

Maryono,

Liantoni

2024

J. Soft Comput. Explor.

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The purpose of this study is to determine the factors that influence the success of student studies in the first year through data mining research using the C4.5 algorithm. This research is a type of quantitative research. This research uses student data of a study program as much as 85 data which will be processed using the Weka application. The data obtained will then be processed using the C4.5 data mining method to produce a decision tree containing rules to predict the success of student studies in the first year. The best result using percentage-split 80% obtained an accuracy of 82.35% as well as the rules contained in the decision tree. The most important factor in determining the success of studies in first-year students is the selection of college entrance pathways. Other factors that become other determinants are education before college, intensity of communication with friends, class year, intensity of off-campus organizations, and plans to change study programs.

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The Implementation of Z-Score Normalization and Boosting Techniques to Increase Accuracy of C4.5 Algorithm in Diagnosing Chronic Kidney Disease

Cited by 15 publications

References 7 publications

Application of a data stratification approach in computer medical monitoring systems

Application of a data stratification approach in computer medical monitoring systems

Comparison of EEG and EOG signals in classification of sleep stages

Prediction of PTIK students' study success in the first year using the c4.5 algorithm

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