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This study focuses on the mosquito borne diseases including dengue-1, dengue-4, yellow fever, west nile virus infection, and filariasis. These are the diseases that are typically shown in the African continent and Eastern Asia, which are the places that suffer from poverty the most. Vaccines for some of the diseases have already been made but the ones who inhabit in those areas do not have the ability...
This paper discusses the implementation of a decision support system for the prediction of asthma in a group of children with related medical factors. The system makes use of the survey data that is gathered as part of ISAAC Phase One Study, obtained through questionnaires completed by adolescents at school and at home by the parents of the children. The model is tested on cross-sectional study data...
The growing impact of disease outbreaks has emphasizedthe need for data mining and machine learning techniquesfor their analysis and prediction. To do this effectivelyan elaborate and reliable data management system is required. Unfortunately, such systems do not exist in many developingcountries where the available information can be sparse andnoisy with important factors missing from the data. In...
Nowadays stroke is the third leading cause of mortality of all life periods. The statistics from the Office of the National Economic and Social Development Board (NESDB) between 1994 and 2013 found that the stroke caused 255,307 cases mortality. Period of treatment in stroke patients depends on symptom and damage of organs. It seems to be beneficial if the data analysis method likes data mining can...
Air pollutants are really a hazardous problem in Bangladesh. This paper works on the relationship between the pollutants and the admittance of patients in the medical facilities and analyzes the reason behind the increase of the disease rate in the hospitals. The research collected medical data from the medical center named National Institute of Disease of the Chest and Hospital (NIDCH) that is located...
Cardiovascular disease (CVD) is a big reason of morbidity and mortality in the current living style. Identification of Cardiovascular disease is an important but a complex task that needs to be performed very minutely, efficiently and the correct automation would be very desirable. Every human being can not be equally skillful and so as doctors. All doctors cannot be equally skilled in every sub specialty...
Heart disease prediction is treated as most complicated task in the field of medical sciences. Thus there arises a need to develop a decision support system for detecting heart disease of a patient. In this paper, we propose efficient genetic algorithm hybrid with the back propagation technique approach for heart disease prediction. Today medical field have come a long way to treat patients with various...
Lupus is autoimmune heterogeneous disease and also a multi system disorder which predominantly affects women. There is no specific diagnostic test to predict Lupus and the diagnosis remains a clinical one, depends on a combination of clinical and laboratory features. Data mining is the use of sophisticated data analysis tools to discover previously unknown, hidden, valid patterns and relationships...
Data mining (DM) has a wide range of applications in the health care field. DM can be used to discover hidden patterns among different diagnoses or to predict the disease of patients based on certain number of symptoms. It can be used also to analyze the success major of a given treatment for a group of patients based on a number of characteristics and parameters available. This paper demonstrates...
In this paper, the Electroencephalogram (EEG) and Functional Magnetic Resonance Imaging (FMRI) parameters along with physical, cognitive and psychological parameters altogether used in the detection and diagnosis of five neuropsychiatric diseases. The diseases are considered for analysis and diagnosis are Attention Deficit Hyperactivity Disorder (ADHD), Dementia, Mood Disorder (MD), Obsessive-Compulsive...
Recent survey shows that heart disease is a leading cause of death in India and in world wide. Significant life savings can be achieved, if a timely and cost effective clinical decision system is developed. Adverse reactions occur if a disease is not diagnosed properly. A clinical decision support system can assist health care professionals for early diagnosis of heart disease from patient's medical...
Large amount of medical data leads to the need of intelligent data mining tools in order to extract useful knowledge. Researchers have been using several statistical analysis and data mining techniques to improve the disease diagnosis accuracy in medical healthcare. Heart disease is considered as the leading cause of deaths worldwide over the past 10 years. Several researchers have introduced different...
Health care data collections are usually characterized by an inherent sparseness due to a large cardinality of patient records and a variety of medical treatments usually adopted for a given pathology. Innovative data analytics approaches are needed to effectively extract interesting knowledge from these large collections. This paper presents an explorative data mining approach, based on a density-based...
This study addresses for applying data-mining techniques in diabetes research which gives a rational insight to model predicate patterns that can forecast incidence of Diabetes Mellitus disease (DMD) in human race. Clinical Patient records and Pathological test reports inherently represent data sets which may be applied to data mining for diabetes research. Hidden knowledge rules may be extracted...
Cardiovascular diseases related ● Coronary heart disease, Angina pectoris, congestive heart failure, Cardiomyopathy, congenital heart disease are the first cause of death in the Asian world. The health care industry collects a huge amount of data which is not properly mined and put into optimum use resulting in these hidden patterns and relationships often going unexploited. Advanced...
This article presents a general meta classifier model for Type 2 Diabetes Mellitus (T2DM) comorbidities which is based on business intelligence and data mining techniques. The proposed meta classifier has two phases: i) the model predicts whether a patient can develop a comorbidity and ii)the model predicts which kind of comorbidity could be: micro or macro vascular. Experiments were carried out with...
Forecasting the dengue fever based on the diagnosis is an important research in order to prevent and control in advance. Such assessment of risk of an epidemic based on the collected information is proposed. An automatic framework is developed for this system based on data mining. This paper present comparison of three reputed decision tree based data mining algorithms such as C4.5, LMT and REPTree...
High and rapidly growing health-care costs pose a challenging problem around the world. Based on the fundamental realities of the country, China developed the single disease payment system. To overcome the drawbacks of a single payment standard, this study uses acute appendicitis as an example to build a decision-tree classification model and to propose a graded charging schedule. The study explored...
The burden of lifestyle diseases such as diabetes have reached epidemic proportions since the last decade in India. An estimated 75 million people in India would become diabetic by 2025. However, the existing healthcare infrastructure is inadequate to meet the demands of this exploding population. Provisioning a web-based patient support system that helps in patient centered decision making and physician...
The healthcare industry collects a huge amount of data which is not properly mined and not put to the optimum use. Discovery of these hidden patterns and relationships often goes unexploited. Our research focuses on this aspect of Medical diagnosis by learning pattern through the collected data of hepatitis and to develop intelligent medical decision support systems to help the physicians. In this...
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