Chronic Hepatitis and Cirrhosis Classification Using SNP Data, Decision Tree and Decision Rule

2007 ◽  
pp. 585-596 ◽  
Author(s):  
Dong-Hoi Kim ◽  
Saangyong Uhmn ◽  
Young-Woong Ko ◽  
Sung Won Cho ◽  
Jae Youn Cheong ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Decision Tree ◽  
Decision Rule ◽  
Snp Data

scholarly journals Classification and Progression Based on CFS-GA and C5.0 Boost Decision Tree of TCM Zheng in Chronic Hepatitis B

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Xiao Yu Chen ◽  
Li Zhuang Ma ◽  
Na Chu ◽  
Min Zhou ◽  
Yiyang Hu
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Decision Tree ◽  
Decision Trees ◽  
Chronic Hepatitis B ◽  
Public Health Problem ◽  
Attribute Selection ◽  
Stepwise Discriminant Analysis ◽  
Boost Decision Tree ◽  
C5.0 Decision Tree

Chronic hepatitis B (CHB) is a serious public health problem, and Traditional Chinese Medicine (TCM) plays an important role in the control and treatment for CHB. In the treatment of TCM, zheng discrimination is the most important step. In this paper, an approach based on CFS-GA (Correlation based Feature Selection and Genetic Algorithm) and C5.0 boost decision tree is used for zheng classification and progression in the TCM treatment of CHB. The CFS-GA performs better than the typical method of CFS. By CFS-GA, the acquired attribute subset is classified by C5.0 boost decision tree for TCM zheng classification of CHB, and C5.0 decision tree outperforms two typical decision trees of NBTree and REPTree on CFS-GA, CFS, and nonselection in comparison. Based on the critical indicators from C5.0 decision tree, important lab indicators in zheng progression are obtained by the method of stepwise discriminant analysis for expressing TCM zhengs in CHB, and alterations of the important indicators are also analyzed in zheng progression. In conclusion, all the three decision trees perform better on CFS-GA than on CFS and nonselection, and C5.0 decision tree outperforms the two typical decision trees both on attribute selection and nonselection.

scholarly journals PERANCANGAN DECISION RULE PADA PRODUKSI KULIT KIKIL SAPI DENGAN MENGGUNAKAN METODE DECISION TREE DI PABRIK TIGA BERSAUDARA

2021 ◽  
Vol 16 (1) ◽  
pp. 63-72
Author(s):  
Bramantiyo Eko Putro ◽  
Tatan Saepurohman
Keyword(s):  
Decision Tree ◽  
Decision Rule ◽  
Raw Materials ◽  
Water Immersion ◽  
Process Data ◽  
Cooking Temperature ◽  
Decision Tree Method ◽  
A Company ◽  
Tree Method

Pabrik Tiga Bersaudara merupakan perusahaan yang bergerak di bidang industri makanan dengan produk  berupa kikil sapi. Persaingan bisnis setiap tahunnya semakin kompetitif, berdasarkan data Badan Pusat Statistik menunjukkan bahwa persaingan bisnis pada komoditas daging sapi di Cianjur mengalami peningkatan. Persaingan tersebut memaksa perusahaan mencari berbagai alternatif untuk unggul. Peningkatan kualitas merupakan salah satu solusi agar dapat bersaing. Penelitian ini bertujuan merancang decision rule untuk produksi kulit kikil sapi di pabrik Tiga Bersaudara. Pengumpulan data dilakukan dengan cara observasi dan wawancara, diperoleh 110 data proses produksi. Data diolah dengan metode decision tree menggunakan RapidMiner dan dilakukan perancangan decision rule. Berdasarkan analisis perancangan decision rule variabel yang perlu dipertimbangkan dalam mementukan kualitas kulit kikil sapi adalah temperatur air pemasakan, lama pemesanan, volume perendaman air 2 dan 3. Aturan keputusan yang terbentuk adalah jika temperatur pemasakan ≤ 95.75° C maka kita harus melihat data lamanya pemesanan bahan baku. Jika waktu pemesanan bahan baku ≤ 18,5 hari, hasilnya akan bagus. Anteseden ini memiliki konsekuensi terbaik yang menghasilkan prediksi benar 48,1% dengan tingkat kemurnian prediksi 100%. Abstract[Decision Rule Design On Beef Knuckle Production Using Decision tree Method In Tiga Bersaudara Factory] Tiga Bersaudara Factory is a company engaged in the food industry with beef knuckles product. Business competition is getting more competitive every year, based on data from the Central Bureau of Statistics, it shows that business competition for beef in Cianjur has increased. This competition forces companies to look for various alternatives to excel. Quality improvement is one solution in order to compete. This study aims to design a decision rule for the production of beef knuckles hides in the Tiga Bersaudara factory. Data collection was carried out by means of observation and interviews, obtained 110 production process data. The data is processed using a decision tree method using Rapid Miner and a decision rule is designed. Based on the analysis of the decision rule design, the variables that need to be considered in determining the quality of cow gravel hull are cooking water temperature, ordering time, water immersion volume 2 and 3.The decision rule that is formed is if the cooking temperature is ≤ 95.75 ° C then we must look at the data on the length of ordering the material. If the ordering time for raw materials is ≤ 18.5 days, the result will be good. This antecedent had the best consequence resulting in a true prediction of 48.1% with a predictive purity level of 100%.Keywords: Decision tree; Decision Rule; Quality; Beef Knuckle

scholarly journals Adaptive interpolation based on optimization of the decision rule in a multidimensional feature space

2020 ◽  
Vol 44 (1) ◽  
pp. 101-108
Author(s):  
M.V. Gashnikov
Keyword(s):  
Decision Tree ◽  
Decision Rule ◽  
Feature Space ◽  
Computational Experiments ◽  
Arbitrary Form ◽  
Multidimensional Signals ◽  
Adaptive Interpolation ◽  
Multidimensional Signal ◽  
Adaptive Interpolator

An adaptive multidimensional signal interpolator is proposed, which selects an interpolating function at each signal point by means of the decision rule optimized in a multidimensional feature space using a decision tree. The search for the dividing boundary when splitting the decision tree vertices is carried out by a recurrence procedure that allows, in addition to the search for the boundary, selecting the best pair of interpolating functions from a predetermined set of functions of an arbitrary form. Results of computational experiments in nature multidimensional signals are presented, confirming the effectiveness of the adaptive interpolator.

scholarly journals Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Somaya Hashem ◽  
Gamal Esmat ◽  
Wafaa Elakel ◽  
Shahira Habashy ◽  
Safaa Abdel Raouf ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis C ◽  
Liver Fibrosis ◽  
Decision Tree ◽  
Chronic Hepatitis C ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Classification Model ◽  
Decision Tree Learning ◽  
Advanced Liver Fibrosis

Background/Aim. Respectively with the prevalence of chronic hepatitis C in the world, using noninvasive methods as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy is significantly increasing. The aim of this study is to combine the serum biomarkers and clinical information to develop a classification model that can predict advanced liver fibrosis.Methods. 39,567 patients with chronic hepatitis C were included and randomly divided into two separate sets. Liver fibrosis was assessed via METAVIR score; patients were categorized as mild to moderate (F0–F2) or advanced (F3-F4) fibrosis stages. Two models were developed using alternating decision tree algorithm. Model 1 uses six parameters, while model 2 uses four, which are similar to FIB-4 features except alpha-fetoprotein instead of alanine aminotransferase. Sensitivity and receiver operating characteristic curve were performed to evaluate the performance of the proposed models.Results. The best model achieved 86.2% negative predictive value and 0.78 ROC with 84.8% accuracy which is better than FIB-4.Conclusions. The risk of advanced liver fibrosis, due to chronic hepatitis C, could be predicted with high accuracy using decision tree learning algorithm that could be used to reduce the need to assess the liver biopsy.

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