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Development of a Cloud-Based Clinical decision Support System for Ophthalmology Triage Using decision tree Artificial Intelligence

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OPHTHALMOLOGY SCIENCE 2023年第1期3卷 100231-100231页

作者： Tanya, Stuti M. Nguyen, Anne X. Buchanan, Sean Jackman, Christopher S. McGill Univ Dept Ophthalmol & Visual Sci Montreal PQ Canada McGill Univ Fac Med Montreal PQ Canada Jackman Eye Inst St John NF Canada Mem Univ Newfoundland Dept Surg Div Ophthalmol St John NF Canada

Purpose: Clinical decision support systems (CDSS) are an emerging frontier in teleophthalmology, drawing on heuristic decision making to augment processes such as triage and referral. We describe the development and implementation of a novel cloud-based decision tree CDSS for on-call ophthalmology consults. The objective was to standardize the triage and referral process while providing a more accurate provisional diagnosis and ***: Prospective comparative cohort ***: On-call referrals to a Canadian community ophthalmology ***: A web-based decision tree algorithm was developed using current guidelines and expert opinion. The algorithm collected tailored information on the patient's ophthalmic concern, and outputted a provisional diagnosis and urgency before sending an electronic referral to the on-call ophthalmology clinic. Data were described using descriptive statistics. Spearman-rho correlations and Cohen's kappa coefficient were used to characterize the observed relationships. Post hoc analysis was conducted using analysis of contingency tables and adjusted *** Outcome Measures: Diagnostic category, provisional diagnosis, and urgency for the referring pro-vider, CDSS, and ophthalmologist. Results: Ninety-six referrals were processed. Referring providers included medical doctors (76.0%, n = 73), optometrists (20.8%, n = 20), and nurse practitioners (3.1%, n = 3). The CDSS (k = 0.5898;95% confidence interval [CI], 0.4868-0.6928;P < 0.0001) performed equally well with 66.7% agreement in determining category when compared with referring providers (k = 0.5880;95% CI, 0.4798-0.6961;P < 0.0001). The CDSS (agreement = 53.1%;k = 0.4999;95% CI, 0.4021-0.5978;P < 0.0001) performed better than referring providers (agreement = 43.8%;k = 0.4191;95% CI, 0.3194-0.5188;P < 0.0001) in determining a diagnosis. The CDSS (r = 0.5014;95% CI, 0.3092-0.6935;P < 0.0001) also performed better than referring providers (r = 0.4035;95% CI, 0.2406-

关键词： Clinical decision support system decision tree algorithm Triage and referral Teleophthalmology Artificial intelligence

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Developing an Integrated Model for Heart Disease Diagnosis (IM-HDD) using ensemble classification methods

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期43卷 4161-4171页

作者： Mohammed, Parves Begum, S. Jabeen JNTUH Sree Chaitanya Coll Engn Hyderabad Telangana India Anna Univ Velalar Coll Engn & Technol Chennai Tamil Nadu India

In present scenario, Heart Disease has become the vital cause of mortality and diagnosis of heart diseases is a great confrontation in the field of medical data analysis. Data Mining is an efficient technique for processing and analyzing larger databases for deriving hidden knowledge appropriately. Hence, it is incorporated in medical data analysis for assisting in effective decision making and disease predictions. With that concern, this paper concentrates on framing an Integrated Model for Heart Disease Diagnosis (IM-HDD) using the advanced data mining conceits. The model considers the significant features of patient data that are available in benchmark datasets. Here, the main objective of the proposed model is to enhance the classification accuracy of patient data on classes under NORMAL and ABNORMAL. For enhancing the classification accuracy, the proposed integrated model utilizes the algorithms such as decision tree algorithm, Naive Baye's Classification and Ensemble Classifiers called Random Forest and Bagging. Further, performance evaluation is performed for analyzing the proposed work. For that, images from UCI repository are utilized and the comparative analysis shows that the proposed work produces better results than the existing models compared.

关键词： Heart disease diagnosis data mining classification accuracy decision tree algorithm Naive Baye's classification random forest and bagging

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Condition Monitoring of Roller Bearing by K-star Classifier andK-nearest Neighborhood Classifier Using Sound Signal

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Structural Durability & Health Monitoring 2017年第1期11卷 1-17页

作者： Rahul Kumar Sharma V.Sugumaran Hemantha Kumar M.Amarnath V.I.T.university ChennaiIndia N.I.T.Karnataka SrinivasanagarSurathkalMangaloreKarnatakaIndia I.I.I.T.D.&M.Jabalpur KhamariaJabalpurIndia Department of Mechanical Engineering School of Mechanical and Building SciencesV.I.T University

Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault ***, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by K-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using K-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both K-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared.

关键词： K-star k-nearest neighborhood k-NN machine learning approach conditionmonitoring fault diagnosis roller bearing decision tree algorithm J-48 random treealgorithm decision making two-layer feature selection sound signal statistical features

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Data Mining-Based Design and Implementation of College Physical Education Performance Management and Analysis System

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2019年第6期14卷 87-97页

作者： Fan, Yimeng Liu, Yu Chen, Haosong Ma, Jianlong Hebei Agr Univ Baoding Peoples R China Baoding Univ Baoding Peoples R China Hebei Agr Univ Coll Mech & Elect Engn Baoding Peoples R China

The purpose of this paper was to effectively apply data mining technology to scientifically analyze the students' physical education (PE) performance so as to serve the physical teaching. The methodology adopted in this paper was to apply *** 3-layer architecture and design and implement college PE performance management and analysis system under the premise of fully analyzing the system requirements based on Visual Studio2008 software development platform and using SQL Server 2005 database platform. Based on data mining technology, students' PE performances were analyzed, and decision tree algorithm was used to make valuable judgments on student performance. The results indicated that applying computer technology to the management and analysis of college PE performance can effectively reduce the teaching and managing workload of PE teachers so that the teachers concentrate more on the quality of physical education.

关键词： Physical education (PE) data mining system requirements decision tree algorithm

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Predicting the effects of frameshifting indels

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GENOME BIOLOGY 2012年第2期13卷 R9-R9页

作者： Hu, Jing Ng, Pauline C. Genome Inst Singapore Singapore 138672 Singapore Franklin & Marshall Coll Dept Math & Comp Sci Lancaster PA 17603 USA

Each human has approximately 50 to 280 frameshifting indels, yet their implications are unknown. We created SIFT Indel, a prediction method for frameshifting indels that has 84% accuracy. The percentage of human frameshifting indels predicted to be gene-damaging is negatively correlated with allele frequency. We also show that although the first frameshifting indel in a gene causes loss of function, there is a tendency for the second frameshifting indel to compensate and restore protein function. SIFT Indel is available at http://***/www/SIFT_***

关键词： Training Dataset Confidence Score Conservation Score decision tree algorithm Human Gene Mutation Database

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Prediction of phishing websites using machine learning

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SPATIAL INFORMATION RESEARCH 2023年第2期31卷 157-166页

作者： Pandey, Mithilesh Kumar Singh, Munindra Kumar Pal, Saurabh Tiwari, B. B. VBS Purvanchal Univ Dept Comp Applicat Jaunpur 222001 Uttar Pradesh India VBS Purvanchal Univ Dept Elect & Commun Jaunpur 222001 Uttar Pradesh India

With the growing popularity of the information science, more application is being integrated with websites that can be accessed directly through the internet. This has increased the possibility of attack by ill-legal persons to steal personal information. To identify a phishing assault, several strategies have been presented. However, there is still opportunity for progress in the fight against phishing. The objective of this research paper is to develop a more accurate prediction model using decision tree (DT), Random Forest (RF) and Gradient Boosting Classifiers (GBC) with three features selection techniques Extra tree (ET), Chi-Square and Recursive Feature Elimination (RFE). Since phishing websites dataset contains 89 features, therefore we have applied extra tree and chi-square, feature selection method to identify the limited important features and then recursive features elimination technique has been used to reduce the dataset up-to optimum important features. We have compared the performance of the developed model using machine learning algorithms and find the best prediction performance using GBC, followed by RF and DT. These algorithmic models capture the trends from various cases of phishing with over R-square, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), in each case.

关键词： Machine learning decision tree algorithm Random forest algorithm Gradient boosting and phishing websites

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Use of a combined transient elastography and biochemical strategy to determine liver fibrosis in pediatric intestinal failure

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CLINICAL NUTRITION 2023年第2期42卷 136-142页

作者： Huysentruyt, Koen Belza, Christina Wong-Sterling, Sylvia Chami, Rose Siddiqui, Iram Wales, Paul W. Avitzur, Yaron Univ Toronto Hosp Sick Children Transplant & Regenerat Med Ctr Grp Improvement Intestinal Funct & Treatment GIFT Toronto ON Canada Vrije Univ Brussel VUB Univ Ziekenhuis Brussel Brussels Ctr Intestinal Failure Children B CIRC Brussels Belgium Univ Toronto Hosp Sick Children Div Pathol Toronto ON Canada Univ Cincinnati Cincinnati Childrens Hosp Med Ctr Div Gen & Thorac Surg Cincinnati OH USA Univ Toronto Hosp Sick Children Div Gastroenterol Hepatol & Nutr Toronto ON Canada Hosp Sick Children Div Gastroenterol Hepatol & Nutr 555 Univ Ave Toronto ON MSG 1X8 Canada

Background & aims: Non-invasive monitoring of intestinal failure (IF) associated liver disease is an ongoing challenge in children with IF. Our objective was to develop a combined algorithm of clinical, transient elastography (TE) and biochemical parameters to identify liver fibrosis in this ***: A retrospective cohort study of IF patients followed by our intestinal rehabilitation program between November 2015 to October 2019. Patients with a liver biopsy and TE were included. De-mographic and liver function tests were collected. Fibrosis on liver biopsies was graded using the modified Scheuer score. decision tree based algorithms classified low (F0-F1) versus high (F2-F4) fibrosis scores based on a combination of TE, biochemical and demographic parameters, using 6-fold classification error, sensitivity and specificity cross-validation (CV) ***: 42 patients (74% male, median age 7.6 (4.6;42.7) months) were evaluated. Median length of PN therapy was 182 (121;556) days. High fibrosis was present in 40.5% with a median TE of 12.1 (6.7;12.9) kPa in high fibrosis children. An algorithm, based on cut-off values for TE of 11.3 kPa and AST of 40 U/L, and grouping of the underlying etiology resulted in a correct classification of 88.1% of the pathology scores;with sensitivity 0.82 (95% CI 0.57;0.96), specificity 0.92 (95% CI 0.74;0.99), positive predictive value 0.88 (95% CI 0.64;0.96) and negative predictive value 0.88 (95% CI 0.73;0.96). The CV classification error was 28.6%, CV sensitivity 72.2% and CV specificity 75.5%.Conclusions: This algorithm shows promising results that could simplify non-invasive monitoring of liver fibrosis in children with IF. Validation in additional IF cohorts is needed.(c) 2022 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

关键词： Children decision tree algorithm Elastography Fibroscan Fibrosis Intestinal failure Intestinal failure associated liver disease (IFALD)

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Automated ASIL Allocation and Decomposition according to ISO 26262, Using the Example of Vehicle Electrical Systems for Automated Driving

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SAE INTERNATIONAL JOURNAL OF PASSENGER CARS-ELECTRONIC AND ELECTRICAL SYSTEMS 2018年第2期11卷 123-130页

作者： Munzing, Patrick Ostertag, Andreas Bertsche, Bernd Koller, Oliver Robert Bosch GmbH Stuttgart Germany Univ Stuttgart Stuttgart Germany

ISO 26262 needs to be considered when developing safety-relevant E/E systems within the automotive industry. One part of the development process according to ISO 26262 is the derivation of the safety requirements for component functions. Here, one attribute of the safety requirements is the Automotive Safety Integrity Level (ASIL). The ASIL at a component level can be determined using ASIL allocation and decomposition. Considering complex systems such as vehicle electrical systems, countless possibilities can be identified for how the ASILs at a component level can be assigned in line with safety goals. In terms of efficiency, manual assignment is not expedient. Therefore, an algorithm for automated assignment of the ASILs will be introduced which considers constraints based on a fault tree analysis. The function of the approach will be demonstrated using the example of a vehicle electrical system from an automated vehicle.

关键词： ISO 26262 Functional safety Safety requirements ASIL assignment ASIL decomposition ASIL allocation decision tree algorithm

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The Implementation and Application of Energy Consumption Monitoring and Management System

The Implementation and Application of Energy Consumption Mon...

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2nd International Conference on Multimedia and Signal Processing (CMSP 2012)

作者： Liu, Dandan Ma, Xingyu Liu, Jia Chen, Qijun Tongji Univ Sch Elect & Informat Shanghai 201804 Peoples R China

ISBN: (纸本)9783642352850

An energy consumption monitoring and management system was established in the wind tunnel center of Tongji University. The hardware of the system included electronic measuring instrument, programmable logic controller (PLC) and an energy-saving data collection server, EcoWebServer IL The electronic measuring instrument collected electric parameters of each circuit node and EcoWebServer II monitored and stored the energy consumption data. The software of the system included project management module, system configuration module, system maintenance module and data monitoring and analysis module. In the data analysis module, an energy consumption model was developed with the decision tree algorithm. The model can predict the hourly electricity consumption of the office building in wind tunnel center. The equipment energy consumption also can be analyzed and the warm-up time can be predicted using collected data. The users can reduce the warm-up time to save energy consumption.

关键词： Energy consumption monitoring and management programmable logic controller decision tree algorithm

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Predicting the Mortality of Female Patients suffering from Myocardial Infarction using Data Mining Methods: A Comparison 12

Predicting the Mortality of Female Patients suffering from M...

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IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)

作者： Alcober, Geliza Marie, I Lagman, Ace C. Revano, Teodoro F. FEU Inst Technol Manila Philippines

ISBN: (纸本)9781665419710

Myocardial Infarction (MI) better known as heart attack, is considered as one of the most alarming diseases that used to harm a great percentage of the male populace. However, the number of female patients suffering from the condition that was formerly known as the "old-man disease" is gradually increasing at the present time. Taking that into consideration, the researchers gathered enough data to come up with a predictive model that could be utilized in identifying the risk indicators for the mortality of female patients suffering from MI. By using different tools in data mining that contribute to a lot of great data discoveries up to date, the researchers made use of logistic regression, random forest, and decision tree to evaluate which technique can generate a diagnostic model with a higher accuracy rate. The generated prognostic models were based on a total of 9 significant attributes that were used to determine the risk indicators for the mortality of female patients suffering from MI. Upon conclusion, it turns out that among the three data mining techniques used in this study, logistic regression has the highest accuracy rate of 79% while random forest and decision tree resulted in 77% and 73% respectively. Medical practitioners could also use this study in discovering the characteristics that made up the clusters or groups of Myocardial Infarction patients that survived and characteristics that made up the clusters or groups of Myocardial Infarction patients that didn't. Determining the risk indicators of a female patient surviving MI tailors a more personalized way of treating the disease.

关键词： Myocardial Infarction Logistic Regression Random Forest decision tree algorithm Prognostic Model

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