Diabetes Prediction Using Artificial Neural Network

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

A Classification System for Diabetic Patients with Machine Learning Techniques

International audienceDiabetes mellitus (DM) is a group of metallic disorder characterized by steep levels of blood glucose prolonged over a time. It results the defection in insulin production or improper action of the cells to the insulin produced. It is one of the significant public health care challenge worldwide. Diabetes exists in a body when pancreas does not construct enough hormone insulin or the human body is not being able to use the insulin properly. The diagnosis of diabetes (diagnosis, etiopathophysiology, therapy etc.) need to generate and process the vast amount of data. Data mining techniques have proven its usefulness and effectiveness in order to evaluate the unknown relationships or patterns if exists with such vast data. In the present work, five techniques based on machine learning namely, AdaBoost, LogicBoost, RobustBoost, Naïve Bayes and Bagging have been proposed for the analysis and prediction of DM patients. The proposed techniques are employed on the data set of Pima Indians Diabetes patients. The results computed are found to be very accurate with classification accuracy of 81.77% and 79.69% by bagging and AdaBoost techniques, respectively. Hence, the proposed techniques employed here are highly adorable, effective and efficient in order to predict the DM

Prediction of Covid-19 Multiparametric Biomarkers and Drug Target of Patients for Risk Stratification Using Machine Learning Approach

In the situation of Coronavirus disease 2019 (COVID-19), forecasting disease progression and identifying therapeutic drug targets is critical, especially given the nonattendance of a viable approach for treating severe cases. The preparation cohort revealed promising biomarkers, which were then precisely measured and employed to assess prediction accuracy across validation cohorts. This approach holds significant potential in enhancing understanding of severe COVID-19 and aiding the development of effective treatments. However, ultrasound-guided MRI (US-MRI) is an emerging modality that can noninvasively acquire multi-parametric information on COVID-19 and function without the need for contrast agents. This shows that neural network analysis of US-MRI transports exclusive prognosis data and this significantly improved prognosis performance. Consequently, the research proposed a deep neural network model of an Ensemble Multi-Relational Graph Neural Network (EMR-GNN) to determine the optimal model for predicting vascular biomarkers (CRP, IL-6, ferritin). In the nonappearance of a tailored treatment for this emerging virus, scientists are actively investigating various strategies to curb its replication. This work focuses on identifying potential drug targets, drawing from proteins abundant in lung material and those targeted by FDA-approved drugs as catalogued in HPA. This effort reflects a broader initiative within the methodical unrestricted to develop effective means of limiting virus replication. Accordingly, recognized five lung-improved proteins, comprising MRC1, SG3A1, CCL18, histone H4, and CLEC3B, were annotated as “drug targets”. For this, the researcher proposes a Heterogeneous Graph Structural Attention Neural Network (HGS-ANN) model to learn topological information of composite molecules and a Dilated Causal CNN-LSTM model with U-Net layers for modelling spatial-sequential information in Simplified Molecular-Input Line-Entry System (SMILES) sequences of drug data. The COVID-19 datasets are downloaded from the GEO database. These data are evaluated using Matlab software. The proposed work evaluated that the AUC of the work is 0.995, however, the AUC is measured based on sex, age, and chronic diseases. This model has a 0.933 accuracy in the subgroup of slices thicker than 1mm. However, the AUC curve and the classification outcome of the proposed method are compared with the existing rad model, deeper, and KNN models. In comparison to existing methods, the proposed model demonstrates superior performance. This research not only identifies potential therapeutic targets nonetheless also serves to uncover biomarkers crucial for comprehending the pathogenesis of undecorated COVID-19

Extracting Rules for Diagnosis of Diabetes Using Genetic Programming

Background: Diabetes is a global health challenge that cusses high incidence of major social and economic consequences. As such, early prevention or identification of those people at risk is crucial for reducing the problems caused by it. The aim of study was to extract the rules for diabetes diagnosing using genetic programming. Methods: This study utilized the PIMA dataset of the University of California, Irvine. This dataset consists of the information of 768 Pima heritage women, including 500 healthy persons and 268 persons with diabetes. Regarding the missing values and outliers in this dataset, the K-nearest neighbor and k-means methods are applied respectively. Moreover, a genetic programming model (GP) was conducted to diagnose diabetes as well as to determine the most important factors affecting it. Accuracy, sensitivity and specificity of the proposed model on the PIMA dataset were obtained as 79.32, 58.96 and 90.74%, respectively. Results: The experimental results of our model on PIMA revealed that age, PG concentration, BMI, Tri Fold Thick and Serum Ins were effective in diabetes mellitus and increased risk of diabetes. In addition, the good performance of the model coupled with the simplicity and comprehensiveness of the extracted rules is also shown by the experimental results. Conclusions: GPs can effectively implement the rules for diagnosing diabetes. Both BMI and PG Concentration are also the most important factors to increase the risk of suffering from diabetes. Keywords: Diabetes, PIMA, Genetic programming, KNNi, K-means, Missing value, Outlier detection, Rule extraction

Diagnosing Heart Diseases For Type 2 Diabetic Patients By Cascading The Data Mining Techniques

Motivated by the world-wide increasing mortality of heart disease patients each year, researchers have been using data mining techniques to help health care professionals in the diagnosis of heart disease. Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. To review the primary prevention studies that focused on the development, validation and impact assessment of a heart disease risk model, scores or rules that can be applied to patients with type 2 diabetes. Efficient predictive modeling is required for medical researchers and practitioners. Attribute values measurement using entropy and information gain parameters. This study proposes Hybrid type 2 diabetes Prediction Model which uses Improved Fuzzy C Means (IFCM) clustering algorithm aimed at validating chosen class label of given data in which incorrectly classified instances are removed and. pattern extracted from original data. Support Vector Machine (SVM) algorithm is used to build the final classifier model by using the k-fold cross-validation method. The aim of this paper is to highlight all the techniques and risk factors that are considered for diagnosis of heart disease. This paper will provide a roadmap for researchers seeking to understand existing automated diagnosis of heart disease

Extracting Rules for Diagnosis of Diabetes Using Genetic Programming

Background: Diabetes is a global health challenge that cusses high incidence of major social and economic consequences. As such, early prevention or identification of those people at risk is crucial for reducing the problems caused by it. The aim of study was to extract the rules for diabetes diagnosing using genetic programming. Methods: This study utilized the PIMA dataset of the University of California, Irvine. This dataset consists of the information of 768 Pima heritage women, including 500 healthy persons and 268 persons with diabetes. Regarding the missing values and outliers in this dataset, the K-nearest neighbor and k-means methods are applied respectively. Moreover, a genetic programming model (GP) was conducted to diagnose diabetes as well as to determine the most important factors affecting it. Accuracy, sensitivity and specificity of the proposed model on the PIMA dataset were obtained as 79.32, 58.96 and 90.74%, respectively. Results: The experimental results of our model on PIMA revealed that age, PG concentration, BMI, Tri Fold Thick and Serum Ins were effective in diabetes mellitus and increased risk of diabetes. In addition, the good performance of the model coupled with the simplicity and comprehensiveness of the extracted rules is also shown by the experimental results. Conclusions: GPs can effectively implement the rules for diagnosing diabetes. Both BMI and PG Concentration are also the most important factors to increase the risk of suffering from diabetes. Keywords: Diabetes, PIMA, Genetic programming, KNNi, K-means, Missing value, Outlier detection, Rule extraction

Computing resources sensitive parallelization of neural neworks for large scale diabetes data modelling, diagnosis and prediction

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Diabetes has become one of the most severe deceases due to an increasing number of diabetes patients globally. A large amount of digital data on diabetes has been collected through various channels. How to utilize these data sets to help doctors to make a decision on diagnosis, treatment and prediction of diabetic patients poses many challenges to the research community. The thesis investigates mathematical models with a focus on neural networks for large scale diabetes data modelling and analysis by utilizing modern computing technologies such as grid computing and cloud computing. These computing technologies provide users with an inexpensive way to have access to extensive computing resources over the Internet for solving data and computationally intensive problems. This thesis evaluates the performance of seven representative machine learning techniques in classification of diabetes data and the results show that neural network produces the best accuracy in classification but incurs high overhead in data training. As a result, the thesis develops MRNN, a parallel neural network model based on the MapReduce programming model which has become an enabling technology in support of data intensive applications in the clouds. By partitioning the diabetic data set into a number of equally sized data blocks, the workload in training is distributed among a number of computing nodes for speedup in data training. MRNN is first evaluated in small scale experimental environments using 12 mappers and subsequently is evaluated in large scale simulated environments using up to 1000 mappers. Both the experimental and simulations results have shown the effectiveness of MRNN in classification, and its high scalability in data training. MapReduce does not have a sophisticated job scheduling scheme for heterogonous computing environments in which the computing nodes may have varied computing capabilities. For this purpose, this thesis develops a load balancing scheme based on genetic algorithms with an aim to balance the training workload among heterogeneous computing nodes. The nodes with more computing capacities will receive more MapReduce jobs for execution. Divisible load theory is employed to guide the evolutionary process of the genetic algorithm with an aim to achieve fast convergence. The proposed load balancing scheme is evaluated in large scale simulated MapReduce environments with varied levels of heterogeneity using different sizes of data sets. All the results show that the genetic algorithm based load balancing scheme significantly reduce the makespan in job execution in comparison with the time consumed without load balancing.This work is funded by the EPSRC and China Market Association

2nd International Consensus Report on Gaps & Opportunities for the Clinical Translation of Precision Diabetes Medicine

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for the heterogeneous etiology, clinical presentation, and pathogenesis of common forms of diabetes and risk of complications. This 2nd International Consensus Report on Precision Diabetes Medicine summarize the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; further, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability, and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine