Decision Support System For Prediction Research Articles

Correlation and Linear Regression Analysis (CALRA): A Predictive Decision Support System for LEAE

This study introduces a Decision Support System called Correlation and Linear Regression Analysis –Decision Support System (CALRA-DSS) System. The CALRA-DSS will use Data mining technique in the process of discovering knowledge which in turn can be used to predict future results. CALRA-DSS predicts Students chances whether passed or Failed in the LEAE Licensure Examination. The integration of Data Mining Technique using Pearson-Product Moment Correlation that is used to determine the degree to which two variables are related and Regression Analysis that is used to examine the relationship between and one dependent and one independent variable. The data to be tested by this CALRA-DSS were the April 2016 Agricultural Engineering graduates of ICET, SPAMAST- Digos Campus who participated in the LEAE Mock Board Examination last and took the August 2016 LEAE. The academic records of these graduates were taken from the SPAMAST Electronic Students Information System (eSMS) while the Mock Board data Result was taken from the SPAMAST LEAE Reviewer Committee. It is concluded that with the use of this tool, the ICET Department can implement an intervention program timely before Student would intend to take the LEAE. Based on the Outcome, the results obtained from the Correlation and Regression Analysis and the attributes obtained from eSMS, the identified Academic Predictors has a strong correlation to Mock Board Examination. In general outcome of the study can give a hint to the Students as to which subjects can be considered as predictor variables for their licensure exam scores and hence become their focus of study/review while still studying.

Grapevine Phenology of cv. Touriga Franca and Touriga Nacional in the Douro Wine Region: Modelling and Climate Change Projections

Projections of grapevine phenophases under future climate change scenarios are strategic decision support tools for viticulturists and wine producers. Several phenological models are tested for budburst, flowering, and veraison and for two main grapevine varieties (cv. Touriga Franca and Touriga Nacional) growing in the Douro Demarcated Region. Four forcing models (Growing degree-days, Richardson, Sigmoid, and Wang) and three dormancy models (Bidabe, Smoothed Utah and Chuine), with different parameterizations and combinations, are used. New datasets, combing phenology with weather station data, widespread over the Douro wine region, were used for this purpose. The eight best performing models and parameterizations were selected for each phenophase and variety, based on performance metrics. For both cultivars, results revealed moderate performances (0.4 < R2 < 0.7) for budburst, while high performances (R2 > 0.7) were found for flowering and veraison, particularly when Growing degree-days or Sigmoid models are used, respectively. Climate change projections were based on a two-member climate model ensemble from the EURO-CORDEX project under RCP4.5. Projections depicted an anticipation of phenophase timings by 6, 8 or 10–12 days until the end of the century for budburst, flowering, and veraison, respectively. The inter-model variability is of approximately 2–4 days for flowering and veraison and 4–6 days for budburst. These results establish grounds for the implementation of a decision support system for monitoring and short-term prediction of grapevine phenology, thus promoting a more efficient viticulture.

Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing

Arrhythmia is a disease-influencing heart and is manifested by an irregular heartbeat. Atrial fibrillation (Afib), atrial flutter (Afl), and ventricular fibrillation (Vfib) are heart arrhythmias affecting predominantly senior citizens. An electrocardiogram (ECG) is a device serving to measure the ECG signal and diagnosis of an abnormal pattern which represents a heartbeat defects. Though it is possible to analyze these signals manually, in some cases it is a difficult task due to the often signal distortion by noise. Furthermore, manual analyzation of patterns is subjective and can lead to an inaccurate diagnosis. An automated computer-aided diagnosis (CAD) is a technique to eliminate these shortcomings. In this work, we present an 6-layer deep convolutional neural network (CNN) for automatic ECG pattern classification of the normal (Nr), Afib, Afl, and Vfib classes. This proposed CNN model requires simple feature extraction and no pre-processing of ECG signals. For two seconds ECG segments, the model obtained the accuracy of 97.78%, specificity and sensitivity of 98.82% and 99.76% respectively. This proposed system can be used as an assistant automatic tool in a clinical environment as a decision support system.

Auto-MeDiSine: an auto-tunable medical decision support engine using an automated class outlier detection method and AutoMLP

With advanced data analysis techniques, efforts for more accurate decision support systems for disease prediction are on the rise. According to the World Health Organization, diabetes-related illnesses and mortalities are on the rise. Hence, early diagnosis is particularly important. In this paper, we present a framework, Auto-MeDiSine, that comprises an automated version of enhanced class outlier detection using a distance-based algorithm (AutoECODB), combined with an ensemble of automatic multilayer perceptron (AutoMLP). AutoECODB is built upon ECODB by automating the tuning of parameters to optimize outlier detection process. AutoECODB cleanses the dataset by removing outliers. Preprocessed dataset is then used to train a prediction model using an ensemble of AutoMLPs. A set of experiments is performed on publicly available Pima Indian Diabetes Dataset as follows: (1) Auto-MeDiSine is compared with other state-of-the-art methods reported in the literature where Auto-MeDiSine realized an accuracy of 88.7%; (2) AutoMLP is compared with other learners including individual (focusing on neural network-based learners) and ensemble learners; and (3) AutoECODB is compared with other preprocessing methods. Furthermore, in order to validate the generality of the framework, Auto-MeDiSine is tested on another publicly available BioStat Diabetes Dataset where it outperforms the existing reported results, reaching an accuracy of 97.1%.

Decision Support System for Breast Cancer Prediction

Decision Support System for Breast Cancer Prediction

Ensemble method based predictive model for analyzing disease datasets: a predictive analysis approach

Medical datasets have attracted the research community for possible analysis and suitable prediction, which helps the human to take proper precautions in preventing future diseases. To perform related operations, data mining techniques have been widely used in developing decision support systems for disease prediction through a set of medical datasets. This work proposes a new predictive model for disease prediction using pre-processing techniques for various disease datasets. The proposed model not only analyses the datasets also improves the performance by using ensemble methods. To process the datasets, pre-processing techniques such as discretization, resampling, principal component, and decision tree have been used. To classify the datasets, classification techniques such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF) have been used. The algorithms are applied with 10 fold validation technique. A predictive analysis has also been performed on various disease datasets, where every dataset results in significant improvement for various performance measures. We perform a predictive analysis on the datasets such as CKD (Chronic Kidney Disease), Cardiovascular Disease (CVD) or heart, Diabetes, Hepatitis disease, Cancer disease and ILPD (Indian Liver Patient disease). Experimental results show that the proposed predictive model outperforms in terms of better accuracy.

An Intelligent Clinical Decision Support System for Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer

PurposeThe aim of this study was to develop and validate a computational clinical decision support system (DSS) on the basis of CT radiomics features for the prediction of lymph node (LN) metastasis in gastric cancer (GC) using machine learning–based analysis. MethodsClinicopathologic and CT imaging data were retrospectively collected from 490 patients who were diagnosed with GC between January 2002 and December 2016. Radiomics features were extracted from venous-phase CT images. Relevant features were selected, ranked, and modeled using a support vector machine classifier in 326 training and validation data sets. A model test was performed independently in a test set (n = 164). Finally, a head-to-head comparison of the diagnostic performance of the DSS and that of the conventional staging criterion was performed. ResultsTwo hundred ninety-seven of the 490 patients examined had histopathologic evidence of LN metastasis, yielding a 60.6% metastatic rate. The area under the curve for predicting LN+ was 0.824 (95% confidence interval, 0.804-0.847) for the DSS in the training and validation data and 0.764 (95% confidence interval, 0.699-0.833) in the test data. The calibration plots showed good concordance between the predicted and observed probability of LN+ using the DSS approach. The DSS was better able to predict LN metastasis than the conventional staging criterion in the training and validation data (accuracy 76.4% versus 63.5%) and in the test data (accuracy 71.3% versus 63.2%) ConclusionsA DSS based on 13 “worrisome” radiomics features appears to be a promising tool for the preoperative prediction of LN status in patients with GC.

Developing a decision support system for osteoporosis Prediction

مقدمه: پوکی استخوان بیماری رایجی در زنان است. شکستگی‌ها آسیب‌های جبران ناپذیری ایجاد می‌کنند. بنابراین، تشخیص زودهنگام این بیماری و شروع درمان قبل از رخ دادن شکستگی مسئله مهمی است. هدف مطالعه حاضر، ایجاد یک سیستم تصمیم یار بالینی مبتنی بر شبکه ­های عصبی مصنوعی با قابلیت تشخیص ابتلا به پوکی استخوان بود. روش کار: مطالعه حاضر از نوع توسعه ای بود که بصورت مقطعی در نیمه دوم سال ۱۳۹۶ انجام شد. در پژوهش حاضر، ابتدا با شناسایی متغیرهای تاثیرگذار، پرسش نامه نظرسنجی برای انتخاب مهم‌ترین عوامل بالینی تهیه شد. اطلاعات ۲۵۶ نفر از زنان و تراکم استخوان پنج سال بعد از ثبت اولیه مربوط به زنان مراجعه کننده به واحد سنجش تراکم استخوان دانشگاه علوم پزشکی بوشهر برای آموزش شبکه استفاده شد. برای یافتن بهترین شبکه از روش آزمون و خطا استفاده شد. همچنین، شبکه با الگوریتم‏های گرادیان مزدوج و لونبرگ-مارکوارت آموزش داده شد. ارزیابی بر اساس ماتریس آشفتگی و معیار‌های حساسیت، ویژگی و صحت انجام گرفت. یافته‌ها: در مرحله اول از ۱۵ ویژگی ضروری از نظر پزشکان، مصرف الکل، انحنای پشت و برداشتن رحم، حذف و ۱۲ متغیر انتخاب شدند. سپس، شبکه عصبی پرسپترون چند‌لایه طراحی شد. نتایج نشان داد که الگوریتم گرادیان مزدوج با ۱۰ نرون و الگوریتم لونبرگ-مارکوارت با ۱۲ نرون در لایه مخفی بهترین ساختار شبکه را دارند. علاوه بر آن ، مقایسه صحت نشان داد که در مجموع الگوریتم لونبرگ-مارکوارت نتایج بهتری داشت. بهترین نتیجه به دست آمده حساسیت، ویژگی و صحت به ترتیب ۱/۸۳، ۴/۸۹ و ۳/۸۶ بود. نتیجه گیری: در مطالعه حاضر، با تمرکز بر داده‌های بومی ابزاری توسعه داده شد که می‌تواند در زمینه پیگیری بیماری پوکی استخوان بسیار موثر باشد. استفاده از این ابزار برای ارجاع به موقع افراد بیمار و شروع درمان می‌تواند از رخ دادن شکستگی عوارض جبران ناپذیر پوکی استخوان جلوگیری کند.

HYBRID DECIDING MODULES WITH VIRTUAL STREAMS FOR CLASSIFICATION AND PREDICTION OF FUNCTIONAL STATE OF COMPLEX SYSTEMS

The problem reviewed of building intelligent decision support systems for classification and prediction of the functional state of complex systems in the article. To predict the state of complex systems, hybrid decision modules with virtual flows are proposed, which reflect the hidden system connections between real and virtual data. The vector of informative features at the input of the hybrid decision module consists of two subsectors, the first of which corresponds to real flows, and the second - to virtual flows. Simulation modeling of classification processes using latent variables was performed, which allowed to evaluate the effect on the quality of classification of artificially introduced virtual flows. The structure of a neural network model with virtual recurrent-type streams is developed. The structure consists of N consecutively included neural network approximants. The outputs of the previous approximators are combined with the vector of in-formative attributes of the subsequent approximators, which allows forming virtual flows of different dimensions. A method is developed for the formation of non-linear models of virtual flows, characterized by the use of the GMDH-simulation method to obtain models of the influence of real flows on virtual flows, learned through nonlinear adalines. The method makes it possible to form a subvector of latent variables of unlimited dimension. Non-linear models of virtual flows are formed through a method based on the use of GMDH modeling. The method makes it possible to obtain neural network structures built on the basis of GMDH models and nonlinear adalines, which make it possible to form a subvector of latent variables of unlimited dimensionality.

An intelligent decision support system for readmission prediction in healthcare

Abstract Readmission prediction in hospitals is a highly complex task involving multiple risk factors that can vary among different disease groups. We address this issue by implementing multiple cross-validated classification models within an intelligent CDSS to enhance patient discharge management. Depending on the diagnosis, the system selects and applies the appropriate model and visualises the prediction results. In addition, the cost and reimbursement development for each episode are determined. The architecture of the CDSS and the integration of the prediction models are presented in this paper.

Replication and Extension of a Forecasting Decision Support System: An Empirical Examination of the Time Series Complexity Scoring Technique

Replication and Extension of a Forecasting Decision Support System: An Empirical Examination of the Time Series Complexity Scoring Technique

A predictive Decision Support System (DSS) for a microalgae production plant based on Internet of Things paradigm

SummaryThe production of microalgae represents a large and rapidly expanding market with several applications in the fields of food, pharmaceutics, cosmetics, and energy. Microalgae are photosynthetic aquatic microorganisms whose growth is mainly controlled by a few environmental parameters: temperature, light, pH, and nutrient availability. For this reason, monitoring and controlling such parameters is crucial for their production. At the same time, the development of mathematical models to simulate the behavior of biological systems has become a major predictive and control tool of production processes. In this paper, we present an Internet of Things (IoT) system that can couple sensor data collected directly by biotechnological cultivations with a predictive simulation model. The IoT system constitutes the core of a Decision Support System developed to help the end‐user in the management of industrial production processes.

Mobile based application for prediction of diabetes mellitus: FHIR Standard

Presently physicians providing clinical decision along clinical guidelines manually on basis of manual query to patients that involve documentation files of lab reports, medication description etc., that make practice sluggish and arduous. With development of mobile based application provide assistance to physician in providing quick decision in appropriate manner as patient data available any-time from any-where that will enhance quality of care in field of healthcare. HL7 standard promotes FHIR(Fast Health Interoperability Resource) standard and health information technology for mobile-health. Mobile health is not a vertical field, but a kind of horizontal field that has cut various impact of healthcare domains. This application provide the effective health management of patient's electronic health record with ease of user friendly interface, that can be accessed on role based authentication by numbers of actors like patient, physician, clinic staff and patient's family etc. Basically this study projected decision support system for prediction of chronic disease-Diabetic- Mellitus using FHIR(Fast Health Interoperability Resource) as standard for interoperability among hospital information system. Application provide physician's structured series of symptoms/signs as uploaded by patients, laboratory and staff. Series of questioner along with outcomes are formed, all outcomes appraised and tested by team of medical domain experts.

Neuro-Fuzzy Based Medical Decision Support System for Coronary Artery Disease Diagnosis and Risk Level Prediction

Neuro-Fuzzy Based Medical Decision Support System for Coronary Artery Disease Diagnosis and Risk Level Prediction

Big data on the shop-floor: sensor-based decision-support for manual processes

Analytics applications are becoming indispensable in today’s business landscape. Greater data availability from self-monitoring production equipment allows firms to empower individual workers on the shop-floor with powerful decision support solutions. To explore the potential of such solutions, we replicate an important manual leak detection process from high-tech composite manufacturing and augment the system with highly sensitive sensors. Based on this setup we illustrate the main steps and major challenges in developing and instantiating a predictive decision support system. By establishing a scalable and generic feature generation approach as well as leveraging techniques from statistical learning, we are able to improve the forecasts of the leak position by almost 90%. Recognizing that mere forecast information cannot be evaluated with respect to business value, we subsequently embed the problem in an analysis of the underlying searcher path problem. We compare predictive and prescriptive search policies against simple benchmark rules. The data-supported policies dramatically reduce the median as well as the variability of the search time. Based on these findings we posit that prescriptive analytics can and should play a greater role in assisting manual labor in manufacturing environments.

Hybrid Ensemble Framework for Heart Disease Detection and Prediction

Data mining techniques have been widely used in clinical decision support systems for detection and prediction of various diseases. As heart disease is the leading cause of death for both men and women, detection and prediction of the heart disease is one of the most important issues in medical domain and many researchers developed intelligent medical decision support systems to improve the ability of the CAD systems in diagnosing heart disease. However, there are almost no studies investigating capabilities of hybrid ensemble methods in building a detection and prediction model for heart disease. In this work, we investigate the use of hybrid ensemble model in which a more reliable ensemble than basic ensemble models is proposed and leads to better performance than other heart disease prediction models. To evaluate the performance of proposed model, a dataset containing 278 samples from SPECT heart disease database is used that after applying the model on the data, 96% of classification accuracy, 80% of sensitivity and 93% of specificity are obtained that indicates acceptable performance of the proposed hybrid ensemble model in comparison with basic ensemble model as well as other state of the art models.

Validation of PERFoRM reference architecture demonstrating an application of data mining for predicting machine failure

The PERFoRM project aims to develop a reference architecture for Agile Manufacturing Control systems for plug-and-produce devices, robots and machines. The aim of the work is to improve the flexibility of the mechanical manufacturing of the housing parts involved in the production of industrial compressors and gas separators. An industrial demonstrator has been designed to implement a Data Analytics tool that provides rules beneficial for root cause analysis and a decision support system for early prediction of the failures. The tool also identifies key alarms for monitoring the machine condition.

Design of a Clinical Decision Support System for Fracture Prediction Using Imbalanced Dataset.

More than 1 billion people suffer from chronic respiratory diseases worldwide, accounting for more than 4 million deaths annually. Inhaled corticosteroid is a popular medication for treating chronic respiratory diseases. Its side effects include decreased bone mineral density and osteoporosis. The aims of this study are to investigate the association of inhaled corticosteroids and fracture and to design a clinical support system for fracture prediction. The data of patients aged 20 years and older, who had visited healthcare centers and been prescribed with inhaled corticosteroids within 2002–2010, were retrieved from the National Health Insurance Research Database (NHIRD). After excluding patients diagnosed with hip fracture or vertebrate fractures before using inhaled corticosteroid, a total of 11645 patients receiving inhaled corticosteroid therapy were included for this study. Among them, 1134 (9.7%) were diagnosed with hip fracture or vertebrate fracture. The statistical results showed that demographic information, chronic respiratory diseases and comorbidities, and corticosteroid-related variables (cumulative dose, mean exposed daily dose, follow-up duration, and exposed duration) were significantly different between fracture and nonfracture patients. The clinical decision support systems (CDSSs) were designed with integrated genetic algorithm (GA) and support vector machine (SVM) by training and validating the models with balanced training sets obtained by random and cluster-based undersampling methods and testing with the imbalanced NHIRD dataset. Two different objective functions were adopted for obtaining optimal models with best predictive performance. The predictive performance of the CDSSs exhibits a sensitivity of 69.84–77.00% and an AUC of 0.7495–0.7590. It was concluded that long-term use of inhaled corticosteroids may induce osteoporosis and exhibit higher incidence of hip or vertebrate fractures. The accumulated dose of ICS and OCS therapies should be continuously monitored, especially for patients with older age and women after menopause, to prevent from exceeding the maximum dosage.

An analytical method for diseases prediction using machine learning techniques

The use of medical datasets has attracted the attention of researchers worldwide. Data mining techniques have been widely used in developing decision support systems for diseases prediction through a set of medical datasets. In this paper, we propose a new knowledge-based system for diseases prediction using clustering, noise removal, and prediction techniques. We use Classification and Regression Trees (CART) to generate the fuzzy rules to be used in the knowledge-based system. We test our proposed method on several public medical datasets. Results on Pima Indian Diabetes, Mesothelioma, WDBC, StatLog, Cleveland and Parkinson’s telemonitoring datasets show that proposed method remarkably improves the diseases prediction accuracy. The results showed that the combination of fuzzy rule-based, CART with noise removal and clustering techniques can be effective in diseases prediction from real-world medical datasets. The knowledge-based system can assist medical practitioners in the healthcare practice as a clinical analytical method.

Decision Support System for Agricultural Crop Prediction Using Machine Learning Techniques

Food is one of the basic needs of human beings. It depends on Agricultural Sector and its production. Also, Indian food industries are heavily dependent on agricultural yield production. But due to some poor climatically condition and poor yield production, lots of farmers have gone into suicide in the past. Reportedly, more than 59,000 farmers have attempted suicide over the last 30 years. Now a day’s all are talking about the agricultural field and their production, but no one has come forwarded to help them to increase their productivity. More and more researchers are doing research in the field of the agricultural sector. However, the only minimal effort has been made relevant to this. Most of the agricultural departments have lots of data pertaining to the crop yield in the past. Data in this field are huge and represented in the form of structured/unstructured form. Hence there is a need for an efficient technique to process these data and to discover potential information out to it. By analyzing these data one can discover potential crop by considering climate, season, ph level, soil type and many more. Thus, the proposed system will help the farmer in identifying correct crop to increase the production.