Characteristic Pattern Study of Coronary Heart Disease with Qi-yin Deficiency Syndrome Based on Decision Tree

Di Indonesia, penyakit jantung koroner menempati posisi pertama sebagai penyakit yang paling banyak mengakibatkan kematian. Jika gejala penyakit jantung koroner dikenali sejak dini maka dapat dilakukan tindakan antisipasi. Diagnosa dilakukan berdasarkan 6 gejala penyakit jantung koroner yaitu sakit dada, tekanan darah tinggi, kolesterol, kadar gula darah, hasil EKG dan jumlah denjut jantung. Metode yang dipakai adalah Probabilistic Fuzzy Decision Tree (PFDT) dengan algoritma Probabilistic Fuzzy ID3. Hasil keakuratan sistem pakar diagnosa penyakit jantung koroner dengan metode PFDT mencapai 95%.In Indonesia, coronary heart disease the first position as the disease that most resulted in death. If symptoms of coronary heart disease are recognized early on, anticipatory action may be taken. Diagnosis is based on 6 symptoms of coronary heart disease chest pain, high blood pressure, cholesterol, blood sugar, ECG results and heartbeat. The method used is Probabilistic Fuzzy Decision Tree (PFDT) with Probabilistic Fuzzy ID3 algorithm. The result of accuracy of expert system of diagnosis of coronary heart disease by PFDT method reached 95%.

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Study on the relationship between serum high density lipoprotein and Pi-deficiency syndrome in patients with coronary heart disease

Chinese Journal of Integrative Medicine ◽

10.1007/s11655-007-0125-6 ◽

2007 ◽

Vol 13 (2) ◽

pp. 125-127

Author(s):

Dong-hui Liang ◽

Yun-qin Chen ◽

Zhi-liang Li ◽

Xiao-min Li

Keyword(s):

Coronary Heart Disease ◽

Heart Disease ◽

High Density Lipoprotein ◽

Density Lipoprotein ◽

Deficiency Syndrome ◽

High Density ◽

Serum High Density Lipoprotein ◽

Pi Deficiency ◽

The Relationship

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4. Coronary Heart Disease prediction using genetic algorithm based decision tree

Intelligent Decision Support Systems ◽

10.1515/9783110621105-004 ◽

2019 ◽

pp. 71-97 ◽

Cited By ~ 1

Keyword(s):

Genetic Algorithm ◽

Coronary Heart Disease ◽

Heart Disease ◽

Decision Tree ◽

Disease Prediction

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Discovery of Diagnosis Pattern of Coronary Heart Disease with Qi Deficiency Syndrome by theT-Test-Based Adaboost Algorithm

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2011/408650 ◽

2011 ◽

Vol 2011 ◽

pp. 1-7 ◽

Cited By ~ 11

Author(s):

Huihui Zhao ◽

Jianxin Chen ◽

Na Hou ◽

Peng Zhang ◽

Yong Wang ◽

...

Keyword(s):

Data Mining ◽

Coronary Heart Disease ◽

Heart Disease ◽

Cross Validation ◽

Deficiency Syndrome ◽

Biological Parameters ◽

Mining Method ◽

Adaboost Algorithm ◽

Data Mining Method ◽

Validation Technique

Coronary heart disease (CHD) is still the leading cause of death for adults worldwide. Traditional Chinese medicine (TCM) has a history of 1000 years fighting against the disease and provides a complementary and alternative treatment to it. Syndrome is the core of TCM diagnosis and it is traditionally diagnosed based on macroscopic symptoms as well as tongue and pulse recognitions of patients. Establishment of the diagnosis method in the microcosmic level is an urgent and major problem in TCM. The aim of this study was to establish characteristic diagnosis pattern for CHD with Qi deficiency syndrome (QDS). Thirty-four biological parameters were detected in 52 patients having unstable angina (UA) with or without QDS. Then, we presented a novel data mining method,t-test-based Adaboost algorithm, to establish highest prediction accuracy with the least number of biological parameters for UA with QDS. We gained a pattern composed of five biological parameters that distinguishes UA with QDS patients from non-QDS patients. The diagnosis accuracy of the patterns could reach 84.5% based on a 3-fold cross validation technique. Moreover, we included 85 UA cases collected from hospitals located in the north and south of China to further verify the association between the pattern and QDS. The classification accuracy is 83.5%, which keeps consistent with the accuracy obtained by the cross-validation technique. The association between a symptom and the five biological parameters was established by the data mining method and it reached an accuracy of ∼80%. These results showed that thet-test-based Adaboost algorithm might be a powerful technique for diagnosing syndrome in TCM in the context of CHD.

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Study on Qi Deficiency Syndrome Identification Modes of Coronary Heart Disease Based on Metabolomic Biomarkers

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2014/281829 ◽

2014 ◽

Vol 2014 ◽

pp. 1-15 ◽

Cited By ~ 7

Author(s):

Qi Shi ◽

Huihui Zhao ◽

Jianxin Chen ◽

Youlin Li ◽

Zhongfeng Li ◽

...

Keyword(s):

Coronary Heart Disease ◽

Heart Disease ◽

Deficiency Syndrome ◽

Healthy People ◽

Dynamic Responses ◽

Plasma Metabolites ◽

Incidence And Mortality ◽

Exogenous Factors ◽

Concentration Changes ◽

Endogenous Metabolites

Coronary heart disease (CHD) is one of the most important types of heart disease because of its high incidence and mortality. With the era of systems biology bursting into reality, the analysis of the whole biological systems whether they are cells, tissues, organs, or the whole organisms has now become the norm of biological researches. Metabolomics is the branch of science concerned with the quantitative understandings of the metabolite complement of integrated living systems and their dynamic responses to the changes of both endogenous and exogenous factors. The aim of this study is to discuss the characteristics of plasma metabolites in CHD patients and CHD Qi deficiency syndrome patients and explore the composition and concentration changes of the plasma metabolomic biomarkers. The results show that 25 characteristic metabolites related to the CHD patients comparing with the healthy people, and 4 identifiable variables had significant differences between Qi deficiency and non-Qi deficiency patients. On the basis of identifying the different plasma endogenous metabolites between CHD patients and healthy people, we further prompted the metabolic rules, pathogenesis, and biological essence in Qi deficiency syndrome patients.

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