scholarly journals Integrated Prediction System for Chronic Disease Diagnosis to Ensure Better Healthcare

2020 ◽  
pp. 25-39
Author(s):  
Geetha Poornima K. ◽  
Krishna Prasad K.
Keyword(s):  
Health Care ◽  
Chronic Disease ◽  
Chronic Diseases ◽  
Predictive Analytics ◽  
Disease Diagnosis ◽  
Exercise Stress ◽  
Healthcare Sector ◽  
Human Beings ◽  
Healthcare Data ◽  
Using Data

Technology innovation has made life easy for human beings. Technology is being used everywhere. This also extends to the healthcare sector. The healthcare sector produces a large amount of data each minute. Because of privacy issues, much of the data generated is not used and is not publicly accessible. Healthcare data comes from diverse sources hence it will be always varied in nature. Keeping track of such data has become much easier these days. Predictive analysis in healthcare is an emerging technology that identifies the person with poor health where the risks of developing chronic conditions are more likely and provide better solutions in the field of healthcare. Statistical methods and algorithms can be used to predict the disease before the actual symptoms are revealed in humans. By using data analytics algorithms one can easily predict chronic diseases such as obesity, high/low Blood Pressure, diabetes, asthma, cardiopulmonary disorders. Because of an unhealthy diet, lack of proper exercise, stress, consumption of tobacco, alcohol, etc. chronic diseases are most common these days. If the symptoms of chronic diseases are detected in the early stages, there will be less risk of hospitalization by cost-effectively maintaining better health. Big data analysis and health care can be mixed to produce accurate results. The application of predictive analytics in healthcare is highlighted in this paper. It provides a broader analysis in the prevention of different chronic diseases by using predictive analytics. The paper also includes various issues that arise when handling health care data. For each chronic disease, diverse models, techniques, and algorithms are used for predicting and analyzing. The paper comprises a conceptual model that integrates the prediction of most common chronic diseases

scholarly journals The impact of chronic disease and disability on the elderly

1999 ◽  
Vol 55 (3) ◽  
pp. 9-14
Author(s):  
C. J. Eales
Keyword(s):  
Health Care ◽  
Chronic Disease ◽  
Chronic Diseases ◽  
Health Care Systems ◽  
The Elderly ◽  
Therapeutic Interventions ◽  
Elderly Persons ◽  
Care Systems ◽  
And Function ◽  
The Impact

Health care systems for elderly people should aim to delay the onset of illness, reducing the final period of infirmity and illness to the shortest possible time. The most effective way to achieve this is by health education and preventative medicine to maintain mobility and function. Changes in life style even in late life may result in improved health, effectively decreasing the incidence of chronic diseases associated with advancing age. This paper presents the problems experienced by elderly persons with chronic diseases and disabilities with indications for meaningful therapeutic interventions.

scholarly journals The influence of primary health care organizational models on patients’ experience of care in different chronic disease situations

2011 ◽  
Vol 31 (3) ◽  
pp. 109-120 ◽  
Author(s):  
R Pineault ◽  
S Provost ◽  
M Hamel ◽  
A Couture ◽  
JF Levesque
Keyword(s):  
Risk Factors ◽  
Health Care ◽  
Primary Health Care ◽  
Chronic Disease ◽  
Chronic Diseases ◽  
Chronic Conditions ◽  
Chronically Ill ◽  
Organizational Models ◽  
Experience Of Care ◽  
Primary Health

Objectives To examine the extent to which experience of care varies across chronic diseases, and to analyze the relationship of primary health care (PHC) organizational models with the experience of care reported by patients in different chronic disease situations. Methods We linked a population survey and a PHC organizational survey conducted in two regions of Quebec. We identified five groups of chronic diseases and contrasted these with a no–chronic-disease group. Results Accessibility of care is low for all chronic conditions and shows little variation across diseases. The contact and the coordination-integrated models are the most accessible, whereas the single-provider model is the least. Process and outcome indices of care experience are much higher than accessibility for all conditions and vary across diseases, with the highest being for cardiovascular-risk-factors and the lowest for respiratory diseases (for people aged 44 and under). However, as we move from risk factors to more severe chronic conditions, the coordination-integrated and community models are more likely to generate better process of care, highlighting the greater potential of these two models to meet the needs of more severely chronically ill individuals within the Canadian health care system.

scholarly journals Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Theyazn H.H Aldhyani ◽  
Ali Saleh Alshebami ◽  
Mohammed Y. Alzahrani
Keyword(s):  
Machine Learning ◽  
Chronic Disease ◽  
Chronic Diseases ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Upper Approximation ◽  
Healthcare Data ◽  
Ambiguous Objects

Chronic diseases represent a serious threat to public health across the world. It is estimated at about 60% of all deaths worldwide and approximately 43% of the global burden of chronic diseases. Thus, the analysis of the healthcare data has helped health officials, patients, and healthcare communities to perform early detection for those diseases. Extracting the patterns from healthcare data has helped the healthcare communities to obtain complete medical data for the purpose of diagnosis. The objective of the present research work is presented to improve the surveillance detection system for chronic diseases, which is used for the protection of people’s lives. For this purpose, the proposed system has been developed to enhance the detection of chronic disease by using machine learning algorithms. The standard data related to chronic diseases have been collected from various worldwide resources. In healthcare data, special chronic diseases include ambiguous objects of the class. Therefore, the presence of ambiguous objects shows the availability of traits involving two or more classes, which reduces the accuracy of the machine learning algorithms. The novelty of the current research work lies in the assumption that demonstrates the noncrisp Rough K-means (RKM) clustering for figuring out the ambiguity in chronic disease dataset to improve the performance of the system. The RKM algorithm has clustered data into two sets, namely, the upper approximation and lower approximation. The objects belonging to the upper approximation are favourable objects, whereas the ones belonging to the lower approximation are excluded and identified as ambiguous. These ambiguous objects have been excluded to improve the machine learning algorithms. The machine learning algorithms, namely, naïve Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNN), and random forest tree, are presented and compared. The chronic disease data are obtained from the machine learning repository and Kaggle to test and evaluate the proposed model. The experimental results demonstrate that the proposed system is successfully employed for the diagnosis of chronic diseases. The proposed model achieved the best results with naive Bayes with RKM for the classification of diabetic disease (80.55%), whereas SVM with RKM for the classification of kidney disease achieved 100% and SVM with RKM for the classification of cancer disease achieved 97.53 with respect to accuracy metric. The performance measures, such as accuracy, sensitivity, specificity, precision, and F-score, are employed to evaluate the performance of the proposed system. Furthermore, evaluation and comparison of the proposed system with the existing machine learning algorithms are presented. Finally, the proposed system has enhanced the performance of machine learning algorithms.

scholarly journals Self-efficacy among patients with chronic diseases and its associated factors

2018 ◽  
Vol 7 (3) ◽  
pp. 82-88
Author(s):  
Dayana Shakya
Keyword(s):  
Health Care ◽  
Body Mass Index ◽  
Chronic Disease ◽  
Chronic Diseases ◽  
Body Mass ◽  
Health Care Providers ◽  
Self Efficacy ◽  
Care Providers ◽  
Modifiable Factors ◽  
Significant Difference

Background: Chronic diseases are in an increasing trend worldwide. Although, this rise may be due to a number of factors, one reason for the worldwide increase is due to better treatment protocols and higher awareness among patients. The management of chronic disease depends on the patient’s ability to alter the modifi able risk factors. The burden of disease can be decreased with better self- efficacy. Objectives: To assess the self-efficacy among patients with chronic diseases Methodology: In this descriptive, cross sectional study, data was collected purposively from 329 patients with chronic diseases presenting in the Medical outpatient department of Kathmandu Medical College. Face to face interview method was used to collect data using Chronic Disease Self-efficacy Scale and Patient Assessment Chronic Illness Care Questionnaire. Association with selected socio demographic variables were computed with Mann Whitney U and Kruskal Wallis H tests. Results: The mean age of the patients was 62±13 years. Males, those earning, those never admitted in the hospital for their disease and those who exercised were found to have better self-efficacy. There was significant difference in self-efficacy in terms of age, education, marital status, caregivers and body mass index. Self-efficacy showed significant positive correlation with monthly family income and health care provider score whereas significant negative correlation with age and monthly cost of treatment. Conclusion: Self-efficacy of patients with chronic disease can be improved with certain modifiable factors like daily exercise and appropriate body mass index. Younger patients, males, educated, employed and married patients were found to have better self-efficacy. Proper counselling by health care providers also improves self-efficacy.

Waging war on modern chronic diseases: primary prevention through exercise biology

2000 ◽  
Vol 88 (2) ◽  
pp. 774-787 ◽  
Author(s):  
Frank W. Booth ◽  
Scott E. Gordon ◽  
Christian J. Carlson ◽  
Marc T. Hamilton
Keyword(s):  
Physical Activity ◽  
Health Care ◽  
Chronic Disease ◽  
Primary Prevention ◽  
Chronic Diseases ◽  
20Th Century ◽  
Physical Inactivity ◽  
Funding System ◽  
Sedentary Population ◽  
Biology Research

In this review, we develop a blueprint for exercise biology research in the new millennium. The first part of our plan provides statistics to support the contention that there has been an epidemic emergence of modern chronic diseases in the latter part of the 20th century. The health care costs of these conditions were almost two-thirds of a trillion dollars and affected 90 million Americans in 1990. We estimate that these costs are now approaching $1 trillion and stand to further dramatically increase as the baby boom generation ages. We discuss the reaction of the biomedical establishment to this epidemic, which has primarily been to apply modern technologies to stabilize overt clinical problems (e.g., secondary and tertiary prevention). Because this approach has been largely unsuccessful in reversing the epidemic, we argue that more emphasis must be placed on novel approaches such as primary prevention, which requires attacking the environmental roots of these conditions. In this respect, a strong association exists between the increase in physical inactivity and the emergence of modern chronic diseases in 20th century industrialized societies. Approximately 250,000 deaths per year in the United States are premature due to physical inactivity. Epidemiological data have established that physical inactivity increases the incidence of at least 17 unhealthy conditions, almost all of which are chronic diseases or considered risk factors for chronic diseases. Therefore, as part of this review, we present the concept that the human genome evolved within an environment of high physical activity. Accordingly, we propose that exercise biologists do not study “the effect of physical activity” but in reality study the effect of reintroducing exercise into an unhealthy sedentary population that is genetically programmed to expect physical activity. On the basis of healthy gene function, exercise research should thus be viewed from a nontraditional perspective in that the “control” group should actually be taken from a physically active population and not from a sedentary population with its predisposition to modern chronic diseases. We provide exciting examples of exercise biology research that is elucidating the underlying mechanisms by which physical inactivity may predispose individuals to chronic disease conditions, such as mechanisms contributing to insulin resistance and decreased skeletal muscle lipoprotein lipase activity. Some findings have been surprising and remarkable in that novel signaling mechanisms have been discovered that vary with the type and level of physical activity/inactivity at multiple levels of gene expression. Because this area of research is underfunded despite its high impact, the final part of our blueprint for the next millennium calls for the National Institutes of Health (NIH) to establish a major initiative devoted to the study of the biology of the primary prevention of modern chronic diseases. We justify this in several ways, including the following estimate: if the percentage of all US morbidity and mortality statistics attributed to the combination of physical inactivity and inappropriate diet were applied as a percentage of the NIH's total operating budget, the resulting funds would equal the budgets of two full institutes at the NIH! Furthermore, the fiscal support of studies elucidating the scientific foundation(s) targeted by primary prevention strategies in other public health efforts has resulted in an increased efficacy of the overall prevention effort. We estimate that physical inactivity impacts 80–90% of the 24 integrated review group (IRG) topics proposed by the NIH's Panel on Scientific Boundaries for Review, which is currently directing a major restructuring of the NIH's scientific funding system. Unfortunately, the primary prevention of chronic disease and the investigation of physical activity/inactivity and/or exercise are not mentioned in the almost 200 total subtopics comprising the IRGs in the Panel's proposal. We believe this to be a glaring omission by the Panel and contend that the current reorganization of NIH's scientific review and funding system is a golden opportunity to invest in fields that study the biological mechanisms of primary prevention of chronic diseases (such as exercise biology). This would be an investment to avoid US health care system bankruptcy as well as to reduce the extreme human suffering caused by chronic diseases. In short, it would be an investment in the future of health care in the new millennium.

scholarly journals Sociodemographic Factors Influencing the Use of eHealth in People with Chronic Diseases

2019 ◽  
Vol 16 (4) ◽  
pp. 645 ◽  
Author(s):  
Fabienne Reiners ◽  
Janienke Sturm ◽  
Lisette J.W. Bouw ◽  
Eveline J.M. Wouters
Keyword(s):  
Health Care ◽  
Chronic Disease ◽  
Chronic Diseases ◽  
Rural Areas ◽  
Health Care Services ◽  
Sociodemographic Factors ◽  
Care Services ◽  
Computing Machinery ◽  
Different Types ◽  
Lower Education

Alongside the growing number of older persons, the prevalence of chronic diseases is increasing, leading to higher pressure on health care services. eHealth is considered a solution for better and more efficient health care. However, not every patient is able to use eHealth, for several reasons. This study aims to provide an overview of: (1) sociodemographic factors that influence the use of eHealth; and (2) suggest directions for interventions that will improve the use of eHealth in patients with chronic disease. A structured literature review of PubMed, ScienceDirect, Association for Computing Machinery Digital Library (ACMDL), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) was conducted using four sets of keywords: “chronic disease”, “eHealth”, “factors”, and “suggested interventions”. Qualitative, quantitative, and mixed-method studies were included. Four researchers each assessed quality and extracted data. Twenty-two out of 1639 articles were included. Higher age and lower income, lower education, living alone, and living in rural areas were found to be associated with lower eHealth use. Ethnicity revealed mixed outcomes. Suggested solutions were personalized support, social support, use of different types of Internet devices to deliver eHealth, and involvement of patients in the development of eHealth interventions. It is concluded that eHealth is least used by persons who need it most. Tailored delivery of eHealth is recommended.

scholarly journals PREDICTIVE ANALYTICS OF HEALTHCARE DATA

2017 ◽  
Vol 10 (13) ◽  
pp. 333
Author(s):  
Sweetlin Hemalatha ◽  
Apurva Waghmare
Keyword(s):  
Health Care ◽  
Logistic Regression ◽  
Regression Model ◽  
Predictive Analytics ◽  
Healthcare Data ◽  
Future Data ◽  
Medical Emergencies ◽  
Precautionary Measures

Predictive analytics is employed to improve the ability to take precautionary measures during medical emergencies. In health care, the sensor-baseddata are generated daily which can be used to predict future data using regression model. In this paper, pain dataset from integrating data for analysis,anonimyzation, and sharing repository is used for experimenting different machine algorithms. The results show that logistic regression gives moreaccuracy than other algorithms.

Mobile Health Applications Assisting Patients With Chronic Diseases

2018 ◽  
pp. 1391-1417
Author(s):  
Petre Iltchev ◽  
Andrzej Śliwczyński ◽  
Potr Szynkiewicz ◽  
Michał Marczak
Keyword(s):  
Health Care ◽  
Chronic Disease ◽  
Chronic Diseases ◽  
Mobile Health ◽  
Health Care Information ◽  
Application Development ◽  
Mobile Health Application ◽  
Health Care Information Systems ◽  
Mobile Health Applications ◽  
Health Applications

This chapter analyzes the role of m-health applications supporting patients with chronic diseases (based on examples from asthma care). The purpose of the chapter is to describe the mobile health application development cycle. The chapter begins with a presentation of asthma as a chronic disease and its prevalence and costs for society, as a determinant of the role and place of m-health applications in chronic disease management. Subsequent sections analyze trends in the development of health care, information systems, and health care payment systems as components of the environment for the implementation of m-health applications. The chapter focuses on prerequisites for the introduction of this type of solutions, presents existing applications, and discusses how to define the key functionalities and benefits for patients, payers, and doctors. The financing cycle, barriers to implementation, and future trends are also addressed.

Leverage Healthcare Data Assets with Predictive Analytics

2017 ◽  
pp. 823-837
Author(s):  
Nilmini Wickramasinghe ◽  
Hoda Moghimi ◽  
Jonathan L. Schaffer
Keyword(s):  
Predictive Analytics ◽  
Healthcare Sector ◽  
Hospital Data ◽  
Healthcare Organizations ◽  
Data Set ◽  
Private Hospitals ◽  
Healthcare Data ◽  
Disparate Data ◽  
Tools And Techniques ◽  
Decision Efficiency

Multi-spectral data residing in disparate data bases represents a critical raw asset for today's healthcare organizations (). However, in order to gain maximum value from such data, it is essential to apply prudent technology solutions and tailored analytic techniques. The following chapter proposes how the application of bespoke predictive analytic tools and techniques can be designed and then applied to a hospital data warehouse, called the Hospital Casemix Protocol (HCP) Extended data set, in order to improve decision efficiency in the private healthcare sector in Australia. The main objective of this chapter is to present the developed conceptual model to demonstrate inputs, outputs, components, principles and services of predictive analytics for private hospitals.

Mobile Health Applications Assisting Patients with Chronic Diseases

2016 ◽  
pp. 170-196 ◽  
Author(s):  
Petre Iltchev ◽  
Andrzej Śliwczyński ◽  
Potr Szynkiewicz ◽  
Michał Marczak
Keyword(s):  
Health Care ◽  
Chronic Disease ◽  
Chronic Diseases ◽  
Mobile Health ◽  
Health Care Information ◽  
Application Development ◽  
Mobile Health Application ◽  
Health Care Information Systems ◽  
Mobile Health Applications ◽  
Health Applications

This chapter analyzes the role of m-health applications supporting patients with chronic diseases (based on examples from asthma care). The purpose of the chapter is to describe the mobile health application development cycle. The chapter begins with a presentation of asthma as a chronic disease and its prevalence and costs for society, as a determinant of the role and place of m-health applications in chronic disease management. Subsequent sections analyze trends in the development of health care, information systems, and health care payment systems as components of the environment for the implementation of m-health applications. The chapter focuses on prerequisites for the introduction of this type of solutions, presents existing applications, and discusses how to define the key functionalities and benefits for patients, payers, and doctors. The financing cycle, barriers to implementation, and future trends are also addressed.

Sign in / Sign up

Export Citation Format

Share Document