• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.271-280
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• 2018

Recently, big data has been growing rapidly due to the development of IT technology. Especially in the medical field, big data is utilized to provide services such as patient-customized medical care, disease management and disease prediction. In Korea, 'National Health Alarm Service' is provided by National Health Insurance Corporation. However, the prediction model has a problem of short-term prediction within 3 days and unreliability of social data used in prediction model. In order to solve these problems, this paper proposes a disease prediction model using medicine prescription data generated from actual patients. This model predicts the total number of patients and the risk of disease in each region and uses the ARIMA model for long-term predictions.

• Korean Journal of Artificial Intelligence
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• v.11 no.3
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• pp.29-34
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• 2023

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).

• The Plant Pathology Journal
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• v.36 no.1
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• pp.54-66
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• 2020

This study was conducted to evaluate usefulness of numerical weather prediction data generated by the Unified Model (UM) for plant disease forecast. Using the UM06- and UM18-predicted weather data, which were released at 0600 and 1800 Universal Time Coordinated (UTC), respectively, by the Korea Meteorological Administration (KMA), disease forecast on bacterial grain rot (BGR) of rice was examined as compared with the model output based on the automated weather stations (AWS)-observed weather data. We analyzed performance of BGRcast based on the UM-predicted and the AWS-observed daily minimum temperature and average relative humidity in 2014 and 2015 from 29 locations representing major rice growing areas in Korea using regression analysis and two-way contingency table analysis. Temporal changes in weather conduciveness at two locations in 2014 were also analyzed with regard to daily weather conduciveness (C_i) and the 20-day and 7-day moving averages of C_i for the inoculum build-up phase (C_inc) prior to the panicle emergence of rice plants and the infection phase (C_inf) during the heading stage of rice plants, respectively. Based on C_inc and C_inf, we were able to obtain the same disease warnings at all locations regardless of the sources of weather data. In conclusion, the numerical weather prediction data from KMA could be reliable to apply as input data for plant disease forecast models. Weather prediction data would facilitate applications of weather-driven disease models for better disease management. Crop growers would have better options for disease control including both protective and curative measures when weather prediction data are used for disease warning.

• ETRI Journal
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• v.37 no.2
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• pp.222-232
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• 2015

In this paper, we propose a dual-phase approach to improve the process of heart disease prediction in a mobile environment. Firstly, only the confident frequent rules are extracted from a patient's clinical information. These are then used to foretell the possibility of the presence of heart disease. However, in some cases, subjects cannot describe exactly what has happened to them or they may have a silent disease - in which case it won't be possible to detect any symptoms at this stage. To address these problems, data records collected over a long period of time of a patient's heart rate variability (HRV) are used to predict whether the patient is suffering from heart disease. By analyzing HRV patterns, doctors can determine whether a patient is suffering from heart disease. The task of collecting HRV patterns is done by an online artificial neural network, which as well as learning knew knowledge, is able to store and preserve all previously learned knowledge. An experiment is conducted to evaluate the performance of the proposed heart disease prediction process under different settings. The results show that the process's performance outperforms existing techniques such as that of the self-organizing map and gas neural growing in terms of classification and diagnostic accuracy, and network structure.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.171-176
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• 2022

A dramatic rise in the number of people dying from heart disease has prompted efforts to find a way to identify it sooner using efficient approaches. A variety of variables contribute to the condition and even hereditary factors. The current estimate approaches use an automated diagnostic system that fails to attain a high level of accuracy because it includes irrelevant dataset information. This paper presents an effective neural network with convolutional layers for classifying clinical data that is highly class-imbalanced. Traditional approaches rely on massive amounts of data rather than precise predictions. Data must be picked carefully in order to achieve an earlier prediction process. It's a setback for analysis if the data obtained is just partially complete. However, feature extraction is a major challenge in classification and prediction since increased data increases the training time of traditional machine learning classifiers. The work integrates the CNN-MDRP classifier (convolutional neural network (CNN)-based efficient multimodal disease risk prediction with TANFIS (tuned adaptive neuro-fuzzy inference system) for earlier accurate prediction. Perform data cleaning by transforming partial data to informative data from the dataset in this project. The recommended TANFIS tuning parameters are then improved using a Laplace Gaussian mutation-based grasshopper and moth flame optimization approach (LGM²G). The proposed approach yields a prediction accuracy of 98.40 percent when compared to current algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.504-519
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• 2023

Heart disease is becoming the top reason of death all around the world. Diagnosing cardiac illness is a difficult endeavor that necessitates both expertise and extensive knowledge. Machine learning (ML) is becoming gradually more important in the medical field. Most of the works have concentrated on the prediction of cardiac disease, however the precision of the results is minimal, and data integrity is uncertain. To solve these difficulties, this research creates an Integrated Accurate-Secure Heart Disease Prediction (IAS) Model based on Deep Convolutional Neural Networks. Heart-related medical data is collected and pre-processed. Secondly, feature extraction is processed with two factors, from signals and acquired data, which are further trained for classification. The Deep Convolutional Neural Networks (DCNN) is used to categorize received sensor data as normal or abnormal. Furthermore, the results are safeguarded by implementing an integrity validation mechanism based on the hash algorithm. The system's performance is evaluated by comparing the proposed to existing models. The results explain that the proposed model-based cardiac disease diagnosis model surpasses previous techniques. The proposed method demonstrates that it attains accuracy of 98.5 % for the maximum amount of records, which is higher than available classifiers.

• Korean Journal of Community Nutrition
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• v.4 no.3
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• pp.421-430
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• 1999

A neural network system was applied in order to analyze the nutritional and other factors influencing chronic diseases. Five different nutrition evaluation methods including SD Score, %RDA, NAR INQ and %RDA-SD Score were utilized to facilitate nutrient data for the system. Observing top three chronic disease prediction ratio, WHR using SD Score was the most frequently quoted factor revealing the highest predication rate as 62.0%. Other high prediction rates using other data processing methods are as follows. Prediction rate with %RDA, NAR, INQ and %RDA-SD Score were 58.5%(diabetes), 53.5%(hyperlipidemia), 51.6%(diabetes), and 58.0%(diabetes)respectively. Higher prediction rate was observed using either NAR or INQ for obesity as 51.7% and 50.9% compared to the previous result using SD Score. After reviewing appearance rate for all chronic disease and for various data processing method used, it was found that iron and vitamin C were the most frequently cited factors resulting in high prediction rate.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.5
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• pp.938-941
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• 2009

In spite of abundant clinical resources of stroke patients, the objective and logical data analyses or diagnostic systems were not established in oriental medicine. In the present study we tried to develop the statistical diagnostic tool discriminating the subtypes of oriental medicine diagnostic system, syndrome differentiation (SD). Discriminant analysis was carried out using clinical data collected from 1,478 stroke patients with the same subtypes diagnosed identically by two clinical experts with more than 3 year experiences. Numerical discriminant models were constructed using important 61 symptom and syndrome indices. Diagnostic accuracy and prediction rate of 5 SD subtypes: The overall diagnostic accuracy of 5 SD subtypes using 61 indices was 74.22%. According to subtypes, the diagnostic accuracy of "phlegm-dampness" was highest (82.84%), and followed by "qi-deficiency", "fire/heat", "static blood", and "yin-deficiency". On the other hand, the overall prediction rate was 67.12% and that of qi-deficiency was highest (73.75%). Diagnostic accuracy and prediction rate of 4 SD subtypes: The overall diagnostic accuracy and prediction rate of 4 SD subtypes except "static blood" were 75.06% and 71.63%, respectively. According to subtypes, the diagnostic accuracy and prediction rate was highest in the "phlegm-dampness" (82.84%) and qi-deficiency (81.69%), respectively. The statistical discriminant model of constructed using 4 SD subtypes, and 61 indices can be used in the field of oriental medicine contributing to the objectification of SD.

• Journal of the Korea Society of Computer and Information
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• v.21 no.3
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• pp.65-71
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• 2016

To reduce the expenses for development a novel drug, systems biology has been studied actively. Target prediction, a part of systems biology, contributes to finding a new purpose for FDA(Food and Drug Administration) approved drugs and development novel drugs. In this paper, we propose a classification model for predicting novel target genes based on relation between target genes and disease related genes. After collecting known target genes from TTD(Therapeutic Target Database) and disease related genes from OMIM(Online Mendelian Inheritance in Man), we analyzed the effect of target genes on disease related genes based on PPI(Protein-Protein Interactions) network. We focused on the distinguishing characteristics between known target genes and random target genes, and used the characteristics as features for building a classifier. Because our model is constructed using information about only a disease and its known targets, the model can be applied to unusual diseases without similar drugs and diseases, while existing models for finding new drug-disease associations are based on drug-drug similarity and disease-disease similarity. We validated accuracy of the model using LOOCV of ten times and the AUCs were 0.74 on Alzheimer's disease and 0.71 on Breast cancer.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1898-1908
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• 2017

Accurately Diagnosing and managing disease in livestock can help sustainable livestock productivity and maintain human health. Maintaining the health of livestock is an important part of human health. The prediction of calf diseases is carried out by pre-processing the calf biometric data. calf information is used as information for calf birth history, calf biometric information, environmental information on housing, and disease management. It can be developed as an ontology and used as a knowledge base. The Bayesian network was used and inferred in the process of analyzing the correlations of calf diseases. Prediction of diseases based on knowledge of calf disease on calf diseases name, causes, occur timing, care and symptoms, etc., will be able to respond to accurate disease treatment and prevent other livestock from being infected in advance.