A Clinical Decision Support System based on fuzzy rules and classification algorithms for monitoring the physiological parameters of type-2 diabetic patients

The use of different types of Clinical Decision Support Systems (CDSS) makes possible the improvement of the quality of the therapeutic and diagnostic efficiency in health field. Those systems, properly implemented, are able to simulate human expert clinician reasoning in order to suggest decisions on treatment of patients. In this paper, we exploit fuzzy inference machines to improve the quality of the day-by-day clinical care of type-2 diabetic patients of Anti-Diabetes Centre (CAD) of the Local Health Authority ASL Naples 1 (Naples, Italy). All the designed functionalities were developed thanks to the experience on the field, through different phases (data collection and adjustment, Fuzzy Inference System development and its validation on real cases) executed by an interdisciplinary research team comprising doctors, clinicians and IT engineers. The proposed approach also allows the remote monitoring of patients' clinical conditions and, hence, can help to reduce hospitalizations

Predicting Diabetes Mellitus With Machine Learning Techniques

Diabetes mellitus is a chronic disease characterized by hyperglycemia. It may cause many complications. According to the growing morbidity in recent years, in 2040, the world’s diabetic patients will reach 642 million, which means that one of the ten adults in the future is suffering from diabetes. There is no doubt that this alarming figure needs great attention. With the rapid development of machine learning, machine learning has been applied to many aspects of medical health. In this study, we used decision tree, random forest and neural network to predict diabetes mellitus. The dataset is the hospital physical examination data in Luzhou, China. It contains 14 attributes. In this study, five-fold cross validation was used to examine the models. In order to verity the universal applicability of the methods, we chose some methods that have the better performance to conduct independent test experiments. We randomly selected 68994 healthy people and diabetic patients’ data, respectively as training set. Due to the data unbalance, we randomly extracted 5 times data. And the result is the average of these five experiments. In this study, we used principal component analysis (PCA) and minimum redundancy maximum relevance (mRMR) to reduce the dimensionality. The results showed that prediction with random forest could reach the highest accuracy (ACC = 0.8084) when all the attributes were used

Precision Medicine: Viable Pathways to Address Existing Research Gaps

Precision Medicine (PM) seeks to customize medical treatments for patients based on measurable and identifiable characteristics. Unlike personalized medicine, this effort is not intended to result in tailored care for each patient. Instead, this effort seeks to improve overall care within the medical domain by shifting the focus from one-size-fits-all care to optimized care for specified subgroups. In order for the benefits of PM to be expeditiously realized, the diverse skills sets of the scientific community must be brought to bear on the problem. This research effort explores the intersection of quality engineering (QE) and healthcare to outline how existing methodologies within the QE field could support existing PM research goals. Specifically this work examines how to determine the value of patient characteristics for use in disease prediction models with select machine learning algorithms, proposes a method to incorporate patient risk into treatment decisions through the development of performance functions, and investigates the potential impact of incorrect assumptions on estimation methods used in optimization models

Predictors and methological issues in tracking total body fat mass, trunk fat, mass and abdominal fat mass : changes in a weight loss intervention with overweight and obese women.

Doutoramento em Motricidade Humana, especialidade de Saúde e Condição FísicaOne of the purposes of this dissertation was to analyze the usefulness of simple anthropometric measurements in predicting total body fat mass, as well as trunk and abdominal fat regions of interest, assessed by DXA, along with their changes in a weight loss program. Another purpose was to examine the influence of different physical activity dimensions on body weight, total body fat mass, abdominal and trunk fat regions of interest, selected by conventional whole body DXA in premenopausal overweight and obese women. Three studies were conducted within the PESO Program (Promotion of Exercise and Health in Obesity), a behavioural intervention addressed to premenopausal overweight and obese women. Key results show that: a) changes in lifestyle habits during a weight loss intervention may provide a stimulus to reduce trunk fat mass, with special focus on abdominal fat mass; b) abdominal circumference is a better predictor of body fat mass loss than waist circumference; c) baseline values of body weight, BMI, sagital diameter and hip circumference, are able to predict total body fat mass changes, but are unable to predict alterations in more specific depots of body fat estimated by DXA; d) alterations in DXA abdominal fat mass estimations were reasonably detected by all the anthropometric variables, but cannot be used to quantify fat mass loss; e) physical activity variables did not induce changes in total body fat mass and body weight; f) an increase in the total amount of physical activity and the increment of total minutes walking played an important role in the reduction of abdominal fat mass estimated by DXA in obese women. RESUMO: Analisar a utilidade de simples medidas antropométricas na predição da massa gorda corporal total, assim como da massa gorda do tronco e região abdominal, estimadas por uma região de interesse obtida pela DXA, bem como as suas alterações, foi um dos objectivos desta dissertação. Outro dos objectivos desta tese prendeu-se com a análise da influência de diferentes dimensões de actividade física no peso corporal, massa gorda corporal total e regiões de interesse (tronco e abdominal) estimadas pela DXA em mulheres com excesso de peso ou obesas. Três estudos foram realizados com base no Programa PESO (Promoção do Exercício e da Saúde na Obesidade), uma intervenção de modificação comportamental em mulheres com excesso de peso ou obesas. Os resultados destes estudos demonstraram que: a) as alterações no estilo de vida durante uma intervenção de perda de peso podem constituir um estímulo na redução da massa gorda do tronco, em particular da massa gorda abdominal; b) o perímetro abdominal prediz melhor a perda de massa gorda corporal total do que o perímetro da cintura; c) os valores iniciais do peso corporal, IMC, diâmetro sagital e perímetro da anca, são bons predictores das alterações da massa gorda corporal total, mas são ineficazes na predição das alterações dos depósitos mais específicos estimados pela DXA; d) as alterações na estimação da massa gorda abdominal obtida pela DXA foram razoavelmente detectadas por todas as medidas antropométricas, mas estas medidas não podem ser utilizadas na quantificação da perda de massa gorda; e) as variáveis de actividade física não induziram alterações na massa gorda corporal total e no peso corporal; f) um incremento na quantidade total de actividade física e um incremento no número de minutos a caminhar podem ter um papel importante na redução da massa gorda abdominal estimada pela DXA em mulheres obesas

Disease diagnosis in smart healthcare: Innovation, technologies and applications

To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer’s disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed

Comparison of Performance Support Vector Machine Algorithm and Naïve Bayes for Diabetes Diagnosis

Handling in the health sector has now developed a lot in terms of information technology. Many studies in the field of information technology that help in accelerating the performance of management of a health agency or from a work of health workers who require fast and good decision making. In this study a comparison of algorithms was used to diagnose diabetes, which had been used from many previous studies. Support vector machines and naïve bayes become comparison algorithms carried out in this study. The purpose of this study was to look at the performance of the two algorithms and help health workers in better decision making. The level of accuracy, precision, sensivity and specificity of the two algorithms will be the main focus of this research. Comparisons were made using a diabetes dataset taken from the National0 Institute0 of0 Diabetes0 and0 Digestive0 and Kidney0 Diseases with a total sample data of 768 sample data. From the results of calculations and comparisons of support vector machine algorithms have a better average value compared to the naïve Bayes algorithm

Optimizing Exposome-wide Assessments in Cardiometabolic Risk

This thesis is focused on cardiovascular disease (CVD) and type 2 diabetes mellitus (T2D), two concomitant conditions that appear with growing concern. In our work, we aim to improve the identification of individuals at-risk of cardiometabolic disease through the characterization of complex environmental exposures (i.e. diet, physical activity), that temporally vary, and the health effects on cardiometabolic traits and disease. Our projects were based upon the Västerbotten Health Survey (VHU) and the Malmö Diet and Cancer (MDCS) studies, which included extensive data on lifestyle, biological intermediates, and clinical outcomes. In Paper I, we utilized the so-called environmental-wide association approach (EWAS), using longitudinal data from > 31,000 adults in VHU study. Under generalized linear models, from ~ 300 candidate exposures, 11 modifiable variables were associated with most of the cardiometabolic traits; the prioritised variables belonged to smoking, coffee intake, physical activity, alcohol intake, and context-specific lifestyle domains. In Paper II, we implemented a machine learning-based model to identify individuals with variable susceptibility to lifestyle risk factors for T2D and CVD. Individuals with sensitivity to blood lipids, and blood pressure associated predictors were at higher risk to develop cardiometabolic disease. Furthermore, when pooling across sensitive groups from the two cohorts, the findings suggest a particular vulnerable subpopulation with different risk profile. In Paper III, a series of causal-inference experiments from VHU and publicly available genome-wide association study (GWAS) summary statistics were used to triangulate evidence of the direct and mediated effects by adiposity and physical activity, of macronutrient intake (fat, carbohydrates, protein and sugar) and cardiometabolic disease. Using structural equation modelling, the mediation analyses enhanced with Mendelian randomization analysis, showed a likely causal putative association between carbohydrate intake and T2D. In addition, the integrative genomic analyses suggested a candidate causal variant localized to the established T2D gene TCF7L2. In Paper IV, we conducted a systematic review and metanalysis of observational studies, complemented by Mendelian randomization analysis using GWAS summary statistics, investigating causal associations of individuals with high, yet normal, glycaemia associated with cardiovascular complications. Prediabetes was likely causally associated with coronary heart disease; suggesting higher, but not diabetic levels of blood glucose confer a risk, thus, effective preventive strategies may prove successful in prediabetes

A Proposed Method to Identify the Occurrence of Diabetes in Human Body using Machine Learning Technique

Advanced machine-learning techniques are often used for reasoning-based diagnosis and advanced prediction system within the healthcare industry. The methods and algorithms are based on the historical clinical data and factbased Medicare evaluation. Diabetes is a global problem. Each year people are developing diabetes and due to diabetes, a lot of people are going for organ amputation. According to the World Health Organization (WHO), there is a sharp rise in number of people developing diabetes. In 1980, it was estimated that 180 million people with diabetes worldwide. This number has risen from 108 million to 422 million in 2014. WHO also reported that 1.6 million deaths in 2016 due to diabetes. Diabetes occurs due to insufficient production of insulin from pancreas. Several research show that unhealthy diet, smoking, less exercise, Body Mass Index (BMI) are the primary cause of diabetes. This paper shows the use of machine learning that can identify a patient of being diabetic or non-diabetic based on previous clinical data. In this article, a method is shown to analyze and compare the relationship between different clinical parameters such as age, BMI, Diet-chart, systolic Blood Pressure etc. After evaluating all the factors this research work successfully combined all the related factors in a single mathematical equation which is very effective to analyze the risk percentage and risk evaluation based on given input parameters by the participants or users

Differential regulation of the DNA methylome in adults born during the Great Chinese Famine in 1959-1961

Background: Extensive epidemiological studies have established the association between exposure to early-life adversity and health status and diseases in adults. Epigenetic regulation is considered as a key mediator for this phenomenon but analysis on humans is sparse. The Great Chinese Famine lasting from 1958 to 1961 is a natural string of disasters offering a precious opportunity for elucidating the underlying epigenetic mechanism of the long-term effect of early adversity. Methods: Using a high-throughput array platform for DNA methylome profiling, we conducted a case-control epigenome-wide association study on early-life exposure to Chinese famine in 79 adults born during 1959-1961 and compared to 105 unexposed subjects born 1963-1964. Results: The single CpG site analysis of whole epigenome revealed a predominant pattern of decreased DNA methylation levels associated with fetal exposure to famine. Four CpG sites were detected with p < 1e-06 (linked to EHMT1, CNR1, UBXN7 and ESM1 genes), 16 CpGs detected with 1e-06 < p < 1e-05 and 157 CpGs with 1e-05 < p < 1e-04, with a predominant pattern of hypomethylation. Functional annotation to genes and their enriched biological pathways mainly involved neurodevelopment, neuropsychological disorders and metabolism. Multiple sites analysis detected two top-rank differentially methylated regions harboring RNF39 on chromosome 6 and PTPRN2 on chromosome 7, both showing epigenetic association with stress-related conditions. Conclusion: Early-life exposure to famine could mediate DNA methylation regulations that persist into adulthood with broad impacts in the activities of genes and biological pathways. Results from this study provide new clues to the epigenetic embedding of early-life adversity and its impacts on adult health.Peer reviewe