• 제목/요약/키워드: disease forecast

검색결과 63건 처리시간 0.022초

DISEASE FORECAST USING MACHINE LEARNING ALGORITHMS

  • HUSSAIN, MOHAMMED MUZAFFAR;DEVI, S. KALPANA
    • Journal of applied mathematics & informatics
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    • 제40권5_6호
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    • pp.1151-1165
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    • 2022
  • Key drive of information quarrying is to digest liked information starting possible information. With the colossal amount of realities kept in documents, information bases, and stores, in the medical care area, it's inexorably significant, assuming excessive, arising compelling resources aimed at examination besides comprehension like information on behalf of the withdrawal of gen that might assistance in independent direction. Classification is method in information mining; it's characterized as per private, passing on item toward a specific course established happening it is likeness toward past instances of different substances trendy the data collection. In pre-owned recycled four Classification algorithm that incorporate Multi-Layer perception, KSTAR, Bayesian Network and PART to fabricate the grouping replicas arranged the malaria data collection and analyze the replicas, degree their exhibition through Waikato Environment for Knowledge Analysis introduced to Java Development Kit 8, then utilizations outfit's technique trendy promoting presentation of the arrangement methodology. The outcome perceived that Bayesian Network return most elevated exactness of 50.05% when working on followed by Multi-Layer perception, with 49.9% when helping is half, then, at that point, Kstar with precision of 49.44%, 49.5% when supporting individually and PART have lesser precision of 48.1% when helping, The exploration recommended that Bayesian Network is awesome toward remain utilized on Malaria data collection in our sanatoriums.

Study of age specific lung cancer mortality trends in the US using functional data analysis

  • Tharu, Bhikhari;Pokhrel, Keshav;Aryal, Gokarna;Kafle, Ram C.;Khanal, Netra
    • Communications for Statistical Applications and Methods
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    • 제28권2호
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    • pp.119-134
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    • 2021
  • Lung cancer is one of the leading causes of cancer deaths in the world. Investigation of mortality rates is pivotal to adequately understand the determinants causing this disease, allocate public health resources, and apply different control measures. Our study aims to analyze and forecast age-specific US lung cancer mortality trends. We report functions of mortality rates for different age groups by incorporating functional principal component analysis to understand the underlying mortality trend with respect to time. The mortality rates of lung cancer have been higher in men than in women. These rates have been decreasing for all age groups since 1990 in men. The same pattern is observed for women since 2000 except for the age group 85 and above. No significant changes in mortality rates in lower age groups have been reported for both gender. Lung cancer mortality rates for males are relatively higher than females. Ten-year predictions of mortality rates depict a continuous decline for both gender with no apparent change for lower age groups (below 40).

A Study and Analysis of COVID-19 Diagnosis and Approach of Deep Learning

  • R, Mangai Begum
    • International Journal of Computer Science & Network Security
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    • 제22권9호
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    • pp.149-158
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    • 2022
  • The pandemic of Covid-19 (Coronavirus Disease 19) has devastated the world, affected millions of people, and disrupted the world economy. The cause of the Covid19 epidemic has been identified as a new variant known as Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV2). It motives irritation of a small air sac referred to as the alveoli. The alveoli make up most of the tissue in the lungs and fill the sac with mucus. Most human beings with Covid19 usually do no longer improve pneumonia. However, chest x-rays of seriously unwell sufferers can be a useful device for medical doctors in diagnosing Covid19-both CT and X-ray exhibit usual patterns of frosted glass (GGO) and consolidation. The introduction of deep getting to know and brand new imaging helps radiologists and medical practitioners discover these unnatural patterns and pick out Covid19-infected chest x-rays. This venture makes use of a new deep studying structure proposed to diagnose Covid19 by the use of chest X-rays. The suggested model in this work aims to predict and forecast the patients at risk and identify the primary COVID-19 risk variables

Development of K-Maryblyt for Fire Blight Control in Apple and Pear Trees in Korea

  • Mun-Il Ahn;Hyeon-Ji Yang;Sung-Chul Yun
    • The Plant Pathology Journal
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    • 제40권3호
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    • pp.290-298
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    • 2024
  • K-Maryblyt has been developed for the effective control of secondary fire blight infections on blossoms and the elimination of primary inoculum sources from cankers and newly emerged shoots early in the season for both apple and pear trees. This model facilitates the precise determination of the blossom infection timing and identification of primary inoculum sources, akin to Maryblyt, predicting flower infections and the appearance of symptoms on various plant parts, including cankers, blossoms, and shoots. Nevertheless, K-Maryblyt has undergone significant improvements: Integration of Phenology Models for both apple and pear trees, Adoption of observed or predicted hourly temperatures for Epiphytic Infection Potential (EIP) calculation, incorporation of adjusted equations resulting in reduced mean error with 10.08 degree-hours (DH) for apple and 9.28 DH for pear, introduction of a relative humidity variable for pear EIP calculation, and adaptation of modified degree-day calculation methods for expected symptoms. Since the transition to a model-based control policy in 2022, the system has disseminated 158,440 messages related to blossom control and symptom prediction to farmers and professional managers in its inaugural year. Furthermore, the system has been refined to include control messages that account for the mechanism of action of pesticides distributed to farmers in specific counties, considering flower opening conditions and weather suitability for spraying. Operating as a pivotal module within the Fire Blight Forecasting Information System (FBcastS), K-Maryblyt plays a crucial role in providing essential fire blight information to farmers, professional managers, and policymakers.

코로나-19 진행에 따른 SIR 기반 예측모형적용 연구 (Research on Application of SIR-based Prediction Model According to the Progress of COVID-19)

  • 김훈;조상섭;채동우
    • Journal of Information Technology Applications and Management
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    • 제31권1호
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    • pp.1-9
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    • 2024
  • Predicting the spread of COVID-19 remains a challenge due to the complexity of the disease and its evolving nature. This study presents an integrated approach using the classic SIR model for infectious diseases, enhanced by the chemical master equation (CME). We employ a Monte Carlo method (SSA) to solve the model, revealing unique aspects of the SARS-CoV-2 virus transmission. The study, a first of its kind in Korea, adopts a step-by-step and complementary approach to model prediction. It starts by analyzing the epidemic's trajectory at local government levels using both basic and stochastic SIR models. These models capture the impact of public health policies on the epidemic's dynamics. Further, the study extends its scope from a single-infected individual model to a more comprehensive model that accounts for multiple infections using the jump SIR prediction model. The practical application of this approach involves applying these layered and complementary SIR models to forecast the course of the COVID-19 epidemic in small to medium-sized local governments, particularly in Gangnam-gu, Seoul. The results from these models are then compared and analyzed.

기상지수에 의한 벼도열병 예찰의 한 모델 (A Model to Forecast Rice Blast Disease Based on Weather Indexing)

  • 김충회
    • 한국식물병리학회지
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    • 제3권3호
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    • pp.210-216
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    • 1987
  • 미기상 상태에 의하여 벼 도열병을 예찰하기 위한 전산화 예찰모델을 개발하여 그 정확도를 전산모델을 수록한 현지위치형 소형 전산기로서 1984년과 1985년에 걸쳐 포장에서 시험하였다. 건전지 작동형 소형 전산기는 벼 군락내 온도, 습도, 잎이 젖어있는 시간을 계속적으로 측정하여 그 상태를 도열병 발생가능성과 관련하여 평가해서 매일의 병발생가능성 수치(BUS)로 표현한다. 매일의 BUS의 누적치(CBUS)와 두 이병성 품종, M-201과 Brazos에서의 도열병 진전정도와는 밀접한 상관이 있었다. 발병엽율의 logit 치를 CBUS로 회귀하였을 때 평균 결정계수$(R^2)$는 품종과 실험한 해에 따라 $71\%\~91\%$였으며 이것은 시간을 독립변수로 사용하였을 때의 결정계수$61\%\~79\%$에 비하여 현저히 높았다. 결정계수는 M-201에 비하여 생육후기에 포장저항성을 보인 Brazos에서 더 낮았다. 이상의 결과, 현예찰 모델은 실제로 사용가능성이 있지만 앞으로 기주의 저항성이나 병원균 집단의 병원성과 관련한 변수들을 기상환경의 변수와 함께 통합함에 의하여 보다 정확한 예찰모델로 개발할 수 있으리라 생각한다.

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우리나라 남부지방에서의 2014년 벼 이삭도열병 대발생 (Outbreak of Rice Panicle Blast in Southern Provinces of Korea in 2014)

  • 강위수;서명철;홍성준;이경재;이용환
    • 식물병연구
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    • 제25권4호
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    • pp.196-204
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    • 2019
  • 지난 2014년에 전남의 나주, 해남, 영암, 고흥, 장흥 등과, 경남의 밀양, 진주, 고성 등에서 이삭도열병이 심하게 발생하여 벼의 재배면적 대비 발생면적 비율이 전남은 11.0%, 경남은 14.6%에 달하였다. 본 연구에서는 남부지방의 주된 발생지역들에 대하여 2014년 8월의 기상 환경, 품종별 재배 면적 차이, 식물체 질소함량에 따른 이삭도열병 발생 정도에 대하여 분석함으로써, 2014년 남부지방 이삭도열병 대발생의 원인을 구명하였다. 2014년에 각 시도 안에서 10,000 ha 이상 재배된 품종으로는 새누리, 일미벼, 황금누리, 운광, 동진1호, 남평벼 등이 있었다. 2014년의 8월 한 달 동안의 평균기온은 해남과 밀양에서 2018년보다 각각 3.2℃, 3.1℃ 낮고 평년보다 1.5℃, 1.3℃ 낮았다. 강수량은 해남과 밀양에서 2018년보다 각각 70.0%, 42.0% 많고 평년보다 40.1%, 125.7% 많았다. 도열병 감염 위험 예측모형에 의한 8월의 감염 경보 발생일수는 2014년이 2018년보다 많았고, 2014년은 전국적으로 발생일수가 많았다. 식물체의 질소함량은 병 발생이 심한 포장(병든이삭률 60% 이상)이 적은 포장(10% 이하)보다 유의하게 높았다. 결론적으로, 2014년에 특정 지역에서 이삭도열병이 대발생한 것은 감수성 품종이 재배되고 저온과 잦은 강우도 있었지만, 특히 질소질 비료가 다량으로 시용된 점이 특정 필지를 중심으로 다 발생하게 한 주요 원인이었다.

Nonlinear Regression Analysis to Determine Infection Models of Colletotrichum acutatum Causing Anthracnose of Chili Pepper Using Logistic Equation

  • Kang, Wee-Soo;Yun, Sung-Chul;Park, Eun-Woo
    • The Plant Pathology Journal
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    • 제26권1호
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    • pp.17-24
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    • 2010
  • A logistic model for describing combined effects of both temperature and wetness period on appressorium formation was developed using laboratory data on percent appressorium formation of Colletotrichum acutatum. In addition, the possible use of the logistic model for forecasting infection risks was also evaluated as compared with a first-order linear model. A simplified equilibrium model for enzymatic reactions was applied to obtain a temperature function for asymptote parameter (A) of logistic model. For the position (B) and the rate (k) parameters, a reciprocal model was used to calculate the respective temperature functions. The nonlinear logistic model described successfully the response of appressorium formation to the combined effects of temperature and wetness period. Especially the temperature function for asymptote parameter A reflected the response of upper limit of appressorium formation to temperature, which showed the typical temperature response of enzymatic reactions in the cells. By having both temperature and wetness period as independent variables, the nonlinear logistic model can be used to determine the length of wetness periods required for certain levels of appressorium formation under different temperature conditions. The infection model derived from the nonlinear logistic model can be used to calculate infection risks using hourly temperature and wetness period data monitored by automated weather stations in the fields. Compared with the nonlinear infection model, the linear infection model always predicted a shorter wetness period for appressorium formation, and resulted in significantly under- and over-estimation of response at low and high temperatures, respectively.

Epidemiological Characteristics and Prediction of Esophageal Cancer Mortality in China from 1991 to 2012

  • Tang, Wen-Rui;Fang, Jia-Ying;Wu, Ku-Sheng;Shi, Xiao-Jun;Luo, Jia-Yi;Lin, Kun
    • Asian Pacific Journal of Cancer Prevention
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    • 제15권16호
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    • pp.6929-6934
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    • 2014
  • Background: To analyze the mortality distribution of esophageal cancer in China from 1991 to 2012, to forecast the mortality in the future five years, and to provide evidence for prevention and treatment of esophageal cancer. Materials and Methods: Mortality data for esophageal cancer in China from 1991 to 2012 were used to describe its epidemiological characteristics, such as the change of the standardized mortality rate, urban-rural differences, sex and age differences. Trend-surface analysis was used to study the geographical distribution of the mortality. Curve estimation, time series, gray modeling, and joinpoint regression were used to predict the mortality for the next five years in the future. Results: In China, the incidence rate of esophageal cancer from 2007 and the mortality rate of esophageal cancer from 2008 increased yearly, with males at $8.72/10^5$ being higher than females, and the countryside at $15.5/10^5$ being higher than in the city. The mortality rate increased from age 45. Geographical analysis showed the mortality rate increased from southern to eastern China, and from northeast to central China. Conclusions: The incidence rate and the standardized mortality rate of esophageal cancer are rising. The regional disease control for esophageal cancer should be focused on eastern, central and northern regions China, and the key targets for prevention and treatment are rural men more than 45 years old. The mortality of esophageal cancer will rise in the next five years.

Mortality Characteristics and Prediction of Female Breast Cancer in China from 1991 to 2011

  • Shi, Xiao-Jun;Au, William W.;Wu, Ku-Sheng;Chen, Lin-Xiang;Lin, Kun
    • Asian Pacific Journal of Cancer Prevention
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    • 제15권6호
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    • pp.2785-2791
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    • 2014
  • Aims: To analyze time-dependent changes in female breast cancer (BC) mortality in China, forecast the trend in the ensuing 5 years, and provide recommendations for prevention and management. Materials and Methods: Mortality data of breast cancer in China from 1991 to 2011 was used to describe characteristics and distribution, such as the changes of the standardized mortality rate, urban-rural differences and age differences. Trend-surface analysis was used to study the geographical distribution of mortality. In addition, curve estimation, time series modeling, Gray modeling (GM) and joinpoint regression were performed to estimate and predict future trends. Results: In China, the mortality rate of breast cancer has increased yearly since 1991. In addition, our data predicted that the trend will continue to increase in the ensuing 5 years. Rates in urban areas are higher than those in rural areas. Over the past decade, all peak ages for death by breast cancer have been delayed, with the first death peak occurring at 55 to 65 years of age in urban and rural areas. Geographical analysis indicated that mortality rates increased from Southwest to Northeast and from West to East. Conclusions: The standardized mortality rate of breast cancer in China is rising and the upward trend is predicted to continue for the next 5 years. Since this can cause an enormous health impact in China, much better prevention and management of breast cancer is needed. Consequently, disease control centers in China should place more focus on the northeastern, eastern and southeastern parts of China for breast cancer prevention and management, and the key population should be among women between ages 55 to 65, especially those in urban communities.