• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.205-215
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• 2017

The brain magnetic resonance images (MRI) is an important imaging biomarker in Alzheimer's disease (AD) as the cerebral atrophy has been shown to strongly associate with cognitive symptoms. The decrease of volume estimates in different structures of the medial temporal lobe related to memory correlates with the decline of cognitive functions in neurodegenerative diseases. During the past decades several methods have been developed for quantifying the disease related atrophy of hippocampus from MRI. Special effort has been dedicated to separate AD and mild cognitive impairment (MCI) related modifications from normal aging for the purpose of early detection and prediction. We trained a multi-class support vector machine (SVM) with probabilistic outputs on a sample (n = 58) of 20 normal controls (NC), 19 individuals with MCI, and 19 individuals with AD. The model was then applied to the cross-validation of same data set which no labels were known and the predictions. This study presents data on the association between MRI quantitative parameters of hippocampus and its quantitative structural changes examination use on the classification of the diseases.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.283-284
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• 2019

In the last decades patient's suffering with Parkinson's disease is increasing at a rapid rate and as per prediction it will grow more rapidly as old age population is increasing at a rapid rate through out the world. As the performance of wearable sensor based approach reached to a new height as well as powerful machine learning technique provides more accurate result these combination has been widely used for assessment of various neurological diseases. ON state is the state where the effect of medicine is present and OFF state the effect of medicine is reduced or not present at all. Classification of ON/OFF state for the Parkinson's disease is important because the patients could injure them self due to freezing of gait and gait related problems in the OFF state. in this paper wearable sensor based approach has been used to collect the data in ON and OFF state and machine learning techniques are used to automate the classification based on the gait pattern. Supervised machine learning techniques able to provide 97.6% accuracy while classifying the ON/OFF state.

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.21-32
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• 2022

Early-stage diagnosis of Alzheimer's Disease (AD) from Cognitively Normal (CN) patients is crucial because treatment at an early stage of AD can prevent further progress in the AD's severity in the future. Recently, computer-aided diagnosis using magnetic resonance image (MRI) has shown better performance in the classification of AD. However, these methods use a traditional machine learning algorithm that requires supervision and uses a combination of many complicated processes. In recent research, the performance of deep neural networks has outperformed the traditional machine learning algorithms. The ability to learn from the data and extract features on its own makes the neural networks less prone to errors. In this paper, a dense neural network is designed for binary classification of Alzheimer's disease. To create a classifier with better results, we studied result of different activation functions in the prediction. We obtained results from 5-folds validations with combinations of different activation functions and compared with each other, and the one with the best validation score is used to classify the test data. In this experiment, features used to train the model are obtained from the ADNI database after processing them using FreeSurfer software. For 5-folds validation, two groups: AD and CN are classified. The proposed DNN obtained better accuracy than the traditional machine learning algorithms and the compared previous studies for AD vs. CN, AD vs. Mild Cognitive Impairment (MCI), and MCI vs. CN classifications, respectively. This neural network is robust and better.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.3
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• pp.540-547
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• 2009

There needs disease-predictable signs in order to enable preventive diagnosis and therapy. Then traditional Chinese medicine applies various medical diagnostic equipments used in western medicine to diagnosing sub-healthy state. But such data are not originated from inherent oriental medicine, and not obtained easily in ordinary clinical practice. This paper is to provide synopsis of the ante-disease diagno-therapeutics partly and to show predictable data based on the facial shapes and signs, especially of gall bladder's versus bladder's body and masculine versus feminine shape. Ante-disease means not only the complete healthy state, but also the state unseen any symptoms in macrographically in the course of outbreak of disease. It contains two stages, first one is the former state of disease and second one is untransmitted state of disease. The patterns of ante-disease consist of latent disease, pre-disease, transmission type like senescent syndrome, abnormal reactive syndrome(變證), syndrome of transmission and transmutation. The classification with gall bladder and bladder type manifests the differences of shape, color and size of each organ in comparison of the universal and standard figures of the human being. On the other hand, the classification with masculine and feminine shape contrasts the innate sexual difference and the shape, characteristics originated from in itself. These two classification theories have their own pathologic types and syndrome types with each disease so that disease-predictable data can be constructed based on such a relationship. In addition, this diagnostic method by facial shapes and signs is able to be applied to whole stages from prenatal to present state of disease even if the cause and inducement are not clear. Ante-disease diagno-theraputic system by Gall Bladder's versus Bladder's Body and Masculine versus Feminine Shape is getting more important in the chronic and internal disease in comparison of the acute and traumatic disease. So this study is able to make up for the limit of diagnosis on ante-disease in the field of oriental medicine clinic.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.125-132
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• 2023

In this study, we compared the performance of machine learning models for predicting Vapor Pressure Deficits (VPD) in greenhouses that affect pore function and photosynthesis as well as plant growth due to nutrient absorption of plants. For VPD prediction, the correlation between the environmental elements in and outside the greenhouse and the temporal elements of the time series data was confirmed, and how the highly correlated elements affect VPD was confirmed. Before analyzing the performance of the prediction model, the amount and interval of analysis time series data (1 day, 3 days, 7 days) and interval (20 minutes, 1 hour) were checked to adjust the amount and interval of data. Finally, four machine learning prediction models (XGB Regressor, LGBM Regressor, Random Forest Regressor, etc.) were applied to compare the prediction performance by model. As a result of the prediction of the model, when data of 1 day at 20 minute intervals were used, the highest prediction performance was 0.008 for MAE and 0.011 for RMSE in LGBM. In addition, it was confirmed that the factor that most influences VPD prediction after 20 minutes was VPD (VPD_y__71) from the past 20 minutes rather than environmental factors. Using the results of this study, it is possible to increase crop productivity through VPD prediction, condensation of greenhouses, and prevention of disease occurrence. In the future, it can be used not only in predicting environmental data of greenhouses, but also in various fields such as production prediction and smart farm control models.

• Journal of Biomedical Engineering Research
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• v.31 no.5
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• pp.375-384
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• 2010

The purpose of this study is to determine whether there is a correlation between the segmental bio-impedance measurement with the frequency modulations and the life-style disease of obesity. An obesity is not simply the factor for estimating the life-style disease of obesity, but also the risk factor occurring. There are many methods (BMI, WHR, Waist, CT, DEXA, BIA, etc.) for measuring a degree of obesity; the bio-impedance measurement is more economic and more effective than others. The physical examination, the blood test, the medical imaging diagnosis and the bio-impedancemeasurementswithmultiple frequencies for each body parts have been conducted for 77 people. The estimated value has been calculated through a segmental bio-impedance model based on multi-frequency that was created to reflect the highest correlation by analyzing correlation with linear regression analysis method for the measured bio-impedance and the risk factors. Then we compared with the clinical diagnosis. In case of high level cholesterol, low HDL-C and high LDL-C for life-style disease, the sensitivity is 80~100%and the specificity is 83~100%. This study has shown conclusively that bio-impedance can be a possible predictor to analyze the disease risk rate of population and individual health maintenance. And also the multi-frequency segmental bio-impedance can be used as early predictor to estimate the life-style disease of obesity.

• The Korean Journal of Physiology and Pharmacology
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• v.18 no.6
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• pp.447-456
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• 2014

Until now, a disease-modifying therapy (DMT) that has an ability to slow or arrest Alzheimer's disease (AD) progression has not been developed, and all clinical trials involving AD patients enrolled by clinical assessment alone also have not been successful. Given the growing consensus that the DMT is likely to require treatment initiation well before full-blown dementia emerges, the early detection of AD will provide opportunities to successfully identify new drugs that slow the course of AD pathology. Recent advances in early detection of AD and prediction of progression of the disease using various biomarkers, including cerebrospinal fluid (CSF) $A{\beta}_{1-42}$, total tau and p-tau181 levels, and imagining biomarkers, are now being actively integrated into the designs of AD clinical trials. In terms of therapeutic mechanisms, monitoring these markers may be helpful for go/no-go decision making as well as surrogate markers for disease severity or progression. Furthermore, CSF biomarkers can be used as a tool to enrich patients for clinical trials with prospect of increasing statistical power and reducing costs in drug development. However, the standardization of technical aspects of analysis of these biomarkers is an essential prerequisite to the clinical uses. To accomplish this, global efforts are underway to standardize CSF biomarker measurements and a quality control program supported by the Alzheimer's Association. The current review summarizes therapeutic targets of developing drugs in AD pathophysiology, and provides the most recent advances in the clinical utility of CSF biomarkers and the integration of CSF biomarkers in current clinical trials.

• The Plant Pathology Journal
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• v.34 no.3
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• pp.163-170
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• 2018

Strawberry Fusarium wilt disease, caused by Fusarium oxysporum f. sp. fragariae, is the most devastating disease in strawberry production. The pathogen produces chlamydospores which tolerate against harsh environment, fungicide and survive for decades in soil. Development of detection and quantification techniques are regarded significantly in many soilborne pathogens to prevent damage from diseases. In this study, we improved specific-quantitative primers for F. oxysporum f. sp. fragariae to reveal correlation between the pathogen density and the disease severity. Standard curve $r^2$ value of the specific-quantitative primers for qRT-PCR and meting curve were over 0.99 and $80.5^{\circ}C$, respectively. Over pathogen $10^5cfu/g$ of soil was required to cause the disease in both lab and field conditions. With the minimum density to develop the wilt disease, the pathogen affected near 60% in nursery plantation. A biological control microbe agent and soil solarization reduced the pathogen population 2-fold and 1.5-fold in soil, respectively. The developed F. oxysporum f. sp. fragariae specific qRT-PCR protocol may contribute to evaluating soil healthiness and appropriate decision making to control the disease.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.300-308
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• 2018

Objectives: Prompt detection is a cornerstone in the control and prevention of infectious diseases. The Integrated Disease Surveillance Project of India identifies outbreaks, but it does not exactly predict outbreaks. This study was conducted to assess temporal correlation between Google Trends and Integrated Disease Surveillance Programme (IDSP) data and to determine the feasibility of using Google Trends for the prediction of outbreaks or epidemics. Methods: The Google search queries related to malaria, dengue fever, chikungunya, and enteric fever for Chandigarh union territory and Haryana state of India in 2016 were extracted and compared with presumptive form data of the IDSP. Spearman correlation and scatter plots were used to depict the statistical relationship between the two datasets. Time trend plots were constructed to assess the correlation between Google search trends and disease notification under the IDSP. Results: Temporal correlation was observed between the IDSP reporting and Google search trends. Time series analysis of the Google Trends showed strong correlation with the IDSP data with a lag of -2 to -3 weeks for chikungunya and dengue fever in Chandigarh (r > 0.80) and Haryana (r > 0.70). Malaria and enteric fever showed a lag period of -2 to -3 weeks with moderate correlation. Conclusions: Similar results were obtained when applying the results of previous studies to specific diseases, and it is considered that many other diseases should be studied at the national and sub-national levels.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.8
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• pp.903-909
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• 2019

Periodontal disease is observed in many adult persons. However we has not clear know the molecular mechanism and how to treat the disease at the molecular levels. Here, we investigated the molecular differences between periodontal disease and normal controls using gene expression data. In particular, we checked whether the periodontal disease and normal tissues would be classified by machine learning algorithms using gene expression data. Moreover, we revealed the differentially expression genes and their function. As a result, we revealed that the periodontal disease and normal control samples were clearly clustered. In addition, by applying several classification algorithms, such as decision trees, random forests, support vector machines, the two samples were classified well with high accuracy, sensitivity and specificity, even though the dataset was imbalanced. Finally, we found that the genes which were related to inflammation and immune response, were usually have distinct patterns between the two classes.