• Communications for Statistical Applications and Methods
• /
• v.8 no.3
• /
• pp.823-829
• /
• 2001

Even though a rapid development of modem medical science, paralysis disease is a highly dangerous and murderous disease. Shin et al. (1978) constructed the diagnosis expert system which identify a type of the paralysis disease from symptoms of a paralysis disease patients by using the canonical discriminant analysis. The decision tree-based analysis, however, has advantages over the method used in Shin et al. (1998), such as it does not need assumptions - linearity and normality, and suggest appropriate diagnosis procedure which is easily explained. In this paper, we applied the decision tree to construct the model which Identify a type of the paralysis disease.

• Industrial Engineering and Management Systems
• /
• v.4 no.1
• /
• pp.102-108
• /
• 2005

This paper studies medical data classification methods, comparing decision tree and system reconstruction analysis as applied to heart disease medical data mining. The data we study is collected from patients with coronary heart disease. It has 1,723 records of 71 attributes each. We use the system-reconstruction method to weight it. We use decision tree algorithms, such as induction of decision trees (ID3), classification and regression tree (C4.5), classification and regression tree (CART), Chi-square automatic interaction detector (CHAID), and exhausted CHAID. We use the results to compare the correction rate, leaf number, and tree depth of different decision-tree algorithms. According to the experiments, we know that weighted data can improve the correction rate of coronary heart disease data but has little effect on the tree depth and leaf number.

• International Journal of Computer Science & Network Security
• /
• v.24 no.2
• /
• pp.150-154
• /
• 2024

In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.

• Journal of Ecology and Environment
• /
• v.35 no.4
• /
• pp.359-365
• /
• 2012

We investigated leaf disease intensity of Kalopanax septemlobus (prickly castor oil tree) caused by the parasitic fungus Mycosphaerella acanthopanacis, in thirty natural host populations in the Japanese Archipelago. The disease intensity observed for individual trees were analyzed using a generalized additive model as a function of tree size, tree density, climatic terms and spatial trend surface. Individual tree size and conspecific tree density were shown to have significant negative and positive effects on disease intensity, respectively. The findings suggest that the probability of disease infection is partly determined by dispersal of infection agents (ascospores) from the fallen leaves on the ground, which can be enhanced by aggregation of host trees in a forest stand. Regional-scale spatial bias was also present in disease intensity; the populations in northern Japan and southern Kyushu were more severely infected by the fungus than those in southwestern Honshu and Shikoku. Regional variation of disease intensity was explained by both climatic factors and a trend surface term, with a latitudinal cline detected, which increases towards the north. Further research should be conducted in order to understand all of the factors generating the latitudinal cline detected in this study.

• The Plant Pathology Journal
• /
• v.30 no.1
• /
• pp.43-50
• /
• 2014

Microorganisms have many roles in nature. They may act as decomposers that obtain nutrients from dead materials, while some are pathogens that cause diseases in animals, insects, and plants. Some are symbionts that enhance plant growth, such as arbuscular mycorrhizae and nitrogen fixation bacteria. However, roles of plant pathogens and diseases in natural ecosystems are still poorly understood. Thus, the current study addressed this deficiency by investigating possible roles of plant diseases in natural ecosystems, particularly, their positive effects on arthropod diversity. In this study, the model system was the oak tree (Quercus spp.) and the canker disease caused by Annulohypoxylon truncatum, and its effects on arthropod diversity. The oak tree site contained 44 oak trees; 31 had canker disease symptoms while 13 were disease-free. A total of 370 individual arthropods were detected at the site during the survey period. The arthropods belonged to 25 species, 17 families, and seven orders. Interestingly, the cankered trees had significantly higher biodiversity and richness compared with the canker-free trees. This study clearly demonstrated that arthropod diversity was supported by the oak tree canker disease.

• Landscaping Tree
• /
• s.128
• /
• pp.16-21
• /
• 2012

• Landscaping Tree
• /
• s.137
• /
• pp.32-35
• /
• 2013

• Journal of Ecology and Environment
• /
• v.38 no.1
• /
• pp.85-93
• /
• 2015

In 2013, the epidemics of pine wilt disease caused by the pine wood nematodes (Bursaphelenchus xylophilus) resulted in damages to the forests of black pine (Pinus thunbergii Parl.) trees in Jeju Island, Korea. Among the affected trees, an old black pine tree at Cave Temple on Mt. Sanbangsan was included and died due to the prevalence of pine wilt disease. The tree was on Mt. Sanbangsan, which was designated as a National Scenic Place with the Number 77 and was believed to be more than 400 years old in age. By examining the disc of the tree stem obtained from the height of 2 m, we counted the tree rings from 4 different directions and cross-dated the readings by comparing the records of drought simulated from the BROOK Model. Our analysis indicates that the tree seems to have grown since late 1860s. Contrary to the belief of the general public, we can conclude that the age of the tree was estimated to be at maximum 150 years, which means that it was not the same old tree as was shown in the painting of the Tam-Ra-Sun-Ryeok-Do (an old painting book for the Inspection Tour of Jeju Island) published in 1702. Discussion was extended to the life history of the tree in growth and leaning and the measures to protect the tree species from the damages of the pine wilt disease caused by pine wood nematodes.

• Journal of Korean society of Dental Hygiene
• /
• v.24 no.2
• /
• pp.91-98
• /
• 2024

Objectives: This case study aimed to evaluate changes in periodontal pathogens and systemic disease indicators following the adjunctive use of probiotics for periodontal treatment. Methods: Two adults, a 64-year-old male and 71-year-old female, were selected with ethical approval and underwent comprehensive oral and systemic health assessments before and after probiotic intake with periodontal debridement. Results: There was a significant reduction in the periodontal pathogens, particularly Porphyromonas gingivalis and Treponema forsythia, and no adverse systemic indicators were observed. Moreover, a trend toward improved lipid profiles was noted, suggesting a potential positive impact on systemic health. Conclusions: This study shows the potential role of probiotics in enhancing oral health and preventing systemic diseases, thus highlighting the need for further research and clinical trials.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.5
• /
• pp.21-28
• /
• 2022

All health problems that occur in the circulatory system are refer to cardiovascular illness, such as heart and vascular diseases. Deaths from cardiovascular disorders are recorded one third of in total deaths in 2019 worldwide, and the number of deaths continues to rise. Therefore, if it is possible to predict diseases that has high mortality rate with patient's data and AI system, they would enable them to be detected and be treated in advance. In this study, models are produced to predict heart disease, which is one of the cardiovascular diseases, and compare the performance of models with Accuracy, Precision, and Recall, with description of the way of improving the performance of the Decision Tree(Decision Tree, KNN (K-Nearest Neighbor), SVM (Support Vector Machine), and DNN (Deep Neural Network) are used in this study.). Experiments were conducted using scikit-learn, Keras, and TensorFlow libraries using Python as Jupyter Notebook in macOS Big Sur. As a result of comparing the performance of the models, the Decision Tree demonstrates the highest performance, thus, it is recommended to use the Decision Tree in this study.