• Industrial Engineering and Management Systems
• /
• v.16 no.1
• /
• pp.64-79
• /
• 2017

Identifying disease genes from human genome is a critical task in biomedical research. Important biological features to distinguish the disease genes from the non-disease genes have been mainly selected based on traditional feature selection approaches. However, the traditional feature selection approaches unnecessarily consider many unimportant biological features. As a result, although some of the existing classification techniques have been applied to disease gene identification, the prediction performance was not satisfactory. A small set of the most important biological features can enhance the accuracy of disease gene identification, as well as provide potentially useful knowledge for biologists or clinicians, who can further investigate the selected biological features as well as the potential disease genes. In this paper, we propose a new stepwise random forests (SRF) approach for biological feature selection and disease gene identification. The SRF approach consists of two stages. In the first stage, only important biological features are iteratively selected in a forward selection manner based on one-dimensional random forest regression, where the updated residual vector is considered as the current response vector. We can then determine a small set of important biological features. In the second stage, random forests classification with regard to the selected biological features is applied to identify disease genes. Our extensive experiments show that the proposed SRF approach outperforms the existing feature selection and classification techniques in terms of biological feature selection and disease gene identification.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.9
• /
• pp.3583-3597
• /
• 2020

In this paper, a parallel deep learning model using a convolutional neural network and a dilated convolutional neural network is proposed to classify Alzheimer's disease with high accuracy in PET/CT images. The developed model consists of two pipelines, a conventional CNN pipeline, and a dilated convolution pipeline. An input image is sent through both pipelines, and at the end of both pipelines, extracted features are concatenated and used for classifying Alzheimer's disease. Complimentary abilities of both networks provide better overall accuracy than single conventional CNNs in the dataset. Moreover, instead of performing binary classification, the proposed model performs three-class classification being Alzheimer's disease, mild cognitive impairment, and normal control. Using the data received from Dong-a University, the model performs classification detecting Alzheimer's disease with an accuracy of up to 95.51%.

• Journal of Korea Multimedia Society
• /
• v.23 no.2
• /
• pp.216-226
• /
• 2020

In this paper, a stacked convolutional autoencoder model is proposed in order to classify Alzheimer's disease with high accuracy in PET/CT images. The proposed model makes use of the latent space representation - which is also called the bottleneck, of the encoder-decoder architecture: The input image is sent through the pipeline and the encoder part, using stacked convolutional filters, extracts the most useful information. This information is in the bottleneck, which then uses Softmax classification operation to classify between Alzheimer's disease, Mild Cognitive Impairment, and Normal Control. Using the data from Dong-A University, the model performs classification in detecting Alzheimer's disease up to 98.54% accuracy.

• International Journal of Advanced Culture Technology
• /
• v.11 no.3
• /
• pp.9-17
• /
• 2023

Parkinson's disease is a complex neurodegenerative disease characterized by the progressive loss of dopamine-producing neurons in the substantia nigra, resulting in a variety of motor and non-motor symptoms. This study aimed to provide a comprehensive overview of Parkinson's disease, including classification of Parkinson's disease, impairment due to impairment, how disability is assessed, and how it is treated. Major symptoms of Parkinson's disease include tremors, stiffness, bradykinesia, and postural instability, and treatment methods include rehabilitation through drugs, surgical procedures, physical therapy, and occupational therapy. Early diagnosis, individualized treatment interventions, and comprehensive treatment involving a multidisciplinary medical team will be essential to manage Parkinson's disease and improve patients' quality of life. In conclusion, this study will provide comprehensive information on the complex nature of Parkinson's disease and serve as a useful guide for healthcare providers designing treatment plans for Parkinson's patients.

• Quality Improvement in Health Care
• /
• v.12 no.2
• /
• pp.84-92
• /
• 2006

Background : To examine the difference between ICD-10 and The Korean standard classification of disease(oriental medicine), and to aim at improve the practical use as statistical data. It is one of the reason of disease classification. On that account we convert the many to many correspondence presenting classification of oriental medicine into many to one correspondence. Method : The study tracked out 155 patients discharged from the university hospital which is located in Gyeonggi Province and managing hospital and oriental medicine hospital from July to October this year. The period of this study was from August 1 to November 18. We compared correspondence between the two services' diagnosis(hospital services and oriental medicine hospital services) at the same time and attempted many to one correspondence classification. That is for production of statistical data. Result : We investigated the group which have had medical treatment experience of two kinds of services at the same time. The result of this investigation was that the same oriental medicine diagnosis used differently in western medicine diagnosis. 44.5% was accorded with western medicine diagnosis. Correspondence of the western medicine diagnose with the top of the Korean standard classification of disease(oriental medicine) list's western medicine diagnosis was 13.5%. For many to one correspondence classification for statistics, one western medicine diagnosis was selected for one oriental medicine diagnosis. In case of the main diagnosis(I sign) was not enough to explain oriental medicine diagnosis' characteristic, we chose multiple other diagnosis, so other diagnosis(II sign) about patient's cause of disease could be selected for supplement after we examined the patient's records. The statistics was possible with this many to one correspondence. Conclusion : The result of this study about correspondence between western medicine diagnoses and those of oriental medicine confirms that The Korean standard classification of disease(oriental medicine) is hard to be standardized with western medicine diagnosis. Therefore, according to this study, we use new many to one correspondence classification, multiple oriental medicine diagnoses with one ICD-10, which can be used by statistical data.

• 대한핵의학회:학술대회논문집
• /
• 2002.05a
• /
• pp.32-36
• /
• 2002

Lymphoma is a group of neoplastic disease of lymphoid tissues, which can be classified into categories of Hodgkin's disease and non-Hodgkin's lymphoma(NHL). Prognosis of lymphoma depends on the extent of disease(staging) especially in Hodgkin's disease, but also depends on the histologic make up in non-Hodgkin's lymphoma. Although non-Hodgkin's lymphoma is a neoplastic transformation of lymphoid cell it is a collection of disease with merphologically and immunologically diverse make up. Consequently the classification of NHL has changed frequently and evolved according to the progress of immunologic and molecular knowledge added to the original morphologic classification. Lymphoma is a disorder sensitive to chemotherapy which often leads to cure of the disease even in advanced stage, while many other patients die from the progression of disease. Therefore, better understanding in newer classification and sensitive imaging technique, such as PET, in lymphoma will likely lead to the improvement of survival rate.

• The KIPS Transactions:PartD
• /
• v.16D no.1
• /
• pp.11-26
• /
• 2009

In order to diagnose cardiovascular disease, we proposed EP-based(emerging pattern- based) classification technique using multi-parametric diagnosis indexes. We analyzed linear/nonlinear features of HRV for three recumbent postures and extracted four diagnosis indexes from ST-segments to apply the multi-parametric diagnosis indexes. In this paper, classification model using essential emerging patterns for diagnosing disease was applied. This classification technique discovers disease patterns of patient group and these emerging patterns are frequent in patients with cardiovascular disease but are not frequent in the normal group. To evaluate proposed classification algorithm, 120 patients with AP (angina pectrois), 13 patients with ACS(acute coronary syndrome) and 128 normal people data were used. As a result of classification, when multi-parametric indexes were used, the percent accuracy in classifying three groups was turned out to be about 88.3%.

• International Journal of Computer Science & Network Security
• /
• v.23 no.9
• /
• pp.150-156
• /
• 2023

For a doctor, diagnosing a patient's heart disease is not easy. It takes the ability and experience with high flying hours to be able to accurately diagnose the type of patient's heart disease based on the existing factors in the patient. Several studies have been carried out to develop tools to identify types of heart disease in patients. However, most only focus on the results of patient answers and lab results, the rest use only echocardiography data or electrocardiogram results. This research was conducted to test how accurate the results of the classification of heart disease by using two medical data, namely echocardiography and electrocardiogram. Three treatments were applied to the two medical data and analyzed using the decision tree approach. The first treatment was to build a classification model for types of heart disease based on echocardiography and electrocardiogram data, the second treatment only used echocardiography data and the third treatment only used electrocardiogram data. The results showed that the classification of types of heart disease in the first treatment had a higher level of accuracy than the second and third treatments. The accuracy level for the first, second and third treatment were 78.95%, 73.69% and 50%, respectively. This shows that in order to diagnose the type of patient's heart disease, it is advisable to look at the records of both the patient's medical data (echocardiography and electrocardiogram) to get an accurate level of diagnosis results that can be accounted for.

• Journal of Korea Multimedia Society
• /
• v.23 no.10
• /
• pp.1250-1257
• /
• 2020

The early detection of diseases is important in agriculture because diseases are major threats of reducing crop yield for farmers. The shape and color of plant leaf are changed differently according to the disease. So we can detect and estimate the disease by inspecting the visual feature in leaf. This study presents a vision-based leaf classification method for detecting the diseases of tomato crop. ResNet-50 model was used to extract the visual feature in leaf and classify the disease of tomato crop, since the model showed the higher accuracy than the other ResNet models with different depths. We propose a new ensemble approach using several DCNN classifiers that have the same structure but have been trained at different ranges in the DCNN layers. Experimental result achieved accuracy of 97.19% for PlantVillage dataset. It validates that the proposed method effectively classify the disease of tomato crop.

• 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.