Hybrid Ensemble Stacking Techniques for Coronary Artery Disease Prediction Using Machine Learning Algorithms

Throughout history, humanity has been plagued by several outbreaks that have claimed numerous lives. Since coronary artery disease is among the most fatal illnesses that humanity has faced in the modern era, it has been recognized in our time. It links several Coronary Artery Disease (CAD) risk factors to the critical requirement for precise, reliable, and workable methods for early identification and management. In light of this, we suggest a technique called Hybrid Ensemble Stacking that combines Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Ada Boosting for the prediction of CAD illnesses. To combine the forecasts of the basis models, a meta-logistic regression model is utilized. According to a quantitative study, the ensemble model and brute force feature selection method together produce a classification accuracy for heart disease of up to 92.66%. The suggested stacking model has demonstrated its effectiveness and outperforms current methods in the categorization of cardiac disorders. Several classification issues have been solved successfully using ensemble techniques. The suggested method was constructed using the Sani dataset, which contains 303 nearly completed records. Using Min-Max Normalization, the data are pre-processed to making it suitable for a Machine Learning (ML) model. SMOTE and SelectKBest technique were applied to   increases the accuracy and efficiency of a model. Using the metrics such as accuracy, precision, recall, F1, ROC and log-loss, the outcomes produced by the suggested model had the greatest performance

Hybrid Ensemble Stacking Techniques for Coronary Artery Disease Prediction using Machine Learning Algorithms

Throughout history, humanity has been plagued by several outbreaks that have claimed numerous lives. Since coronary artery disease is among the most fatal illnesses that humanity has faced in the modern era, it has been recognized in our time. It links several Coronary Artery Disease (CAD) risk factors to the critical requirement for precise, reliable, and workable methods for early identification and management. In light of this, we suggest a technique called Hybrid Ensemble Stacking that combines Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Ada Boosting for the prediction of CAD illnesses. To combine the forecasts of the basis models, a meta-logistic regression model is utilized. According to a quantitative study, the ensemble model and brute force feature selection method together produce a classification accuracy for heart disease of up to 92.66%. The suggested stacking model has demonstrated its effectiveness and outperforms current methods in the categorization of cardiac disorders. Several classification issues have been solved successfully using ensemble techniques. The suggested method was constructed using the Sani dataset, which contains 303 nearly completed records. Using Min-Max Normalization, the data are pre-processed to making it suitable for a Machine Learning (ML) model. SMOTE and SelectKBest technique were applied to   increases the accuracy and efficiency of a model. Using the metrics such as accuracy, precision, recall, F1, ROC and log-loss, the outcomes produced by the suggested model had the greatest performance

A Review of Prosthetic Interface Stress Investigations

Over the last decade, numerous experimental and numerical analyses have been conducted to investigate the stress distribution between the residual limb and prosthetic socket of persons with lower limb amputation. The objectives of these analyses have been to improve our understanding of the residual limb/prosthetic socket system, to evaluate the influence of prosthetic design parameters and alignment variations on the interface stress distribution, and to evaluate prosthetic fit. The purpose of this paper is to summarize these experimental investigations and identify associated limitations. In addition, this paper presents an overview of various computer models used to investigate the residual limb interface, and discusses the differences and potential ramifications of the various modeling formulations. Finally, the potential and future applications of these experimental and numerical analyses in prosthetic design are presented

Virtual bloXing - assembly rapid prototyping for near net shapes

Virtual reality (VR) provides another dimension to many engineering applications. Its immersive and interactive nature allows an intuitive approach to study both cognitive activities and performance evaluation. Market competitiveness means having products meet form, fit and function quickly. Rapid Prototyping and Manufacturing (RP&M) technologies are increasingly being applied to produce functional prototypes and the direct manufacturing of small components. Despite its flexibility, these systems have common drawbacks such as slow build rates, a limited number of build axes (typically one) and the need for post processing. This paper presents a Virtual Assembly Rapid Prototyping (VARP) project which involves evaluating cognitive activities in assembly tasks based on the adoption of immersive virtual reality along with a novel nonlayered rapid prototyping for near net shape (NNS) manufacturing of components. It is envisaged that this integrated project will facilitate a better understanding of design for manufacture and assembly by utilising equivalent scale digital and physical prototyping in one rapid prototyping system. The state of the art of the VARP project is also presented in this paper

Virtual assembly rapid prototyping of near net shapes

Virtual reality (VR) provides another dimension to many engineering applications. Its immersive and interactive nature allows an intuitive approach to study both cognitive activities and performance evaluation. Market competitiveness means having products meet form, fit and function quickly. Rapid Prototyping and Manufacturing (RP&M) technologies are increasingly being applied to produce functional prototypes and the direct manufacturing of small components. Despite its flexibility, these systems have common drawbacks such as slow build rates, a limited number of build axes (typically one) and the need for post processing. This paper presents a Virtual Assembly Rapid Prototyping (VARP) project which involves evaluating cognitive activities in assembly tasks based on the adoption of immersive virtual reality along with a novel non-layered rapid prototyping for near net shape (NNS) manufacturing of components. It is envisaged that this integrated project will facilitate a better understanding of design for manufacture and assembly by utilising equivalent scale digital and physical prototyping in one rapid prototyping system. The state of the art of the VARP project is also presented in this paper

Multi-Objective Design Optimization of the Leg Mechanism for a Piping Inspection Robot

This paper addresses the dimensional synthesis of an adaptive mechanism of contact points ie a leg mechanism of a piping inspection robot operating in an irradiated area as a nuclear power plant. This studied mechanism is the leading part of the robot sub-system responsible of the locomotion. Firstly, three architectures are chosen from the literature and their properties are described. Then, a method using a multi-objective optimization is proposed to determine the best architecture and the optimal geometric parameters of a leg taking into account environmental and design constraints. In this context, the objective functions are the minimization of the mechanism size and the maximization of the transmission force factor. Representations of the Pareto front versus the objective functions and the design parameters are given. Finally, the CAD model of several solutions located on the Pareto front are presented and discussed.Comment: Proceedings of the ASME 2014 International Design Engineering Technical Conferences \& Computers and Information in Engineering Conference, Buffalo : United States (2014

Edge Chipping Resistance and Flexural Strength of Polymer Infiltrated Ceramic Network and Resin Nanoceramic Restorative Materials

Statement of problem Two novel restorative materials, a polymer infiltrated ceramic network (PICN) and a resin nanoceramic (RNC), for computer-assisted design and computer-assisted manufacturing (CAD-CAM) applications have recently become commercially available. Little independent evidence regarding their mechanical properties exists to facilitate material selection. Purpose The purpose of this in vitro study was to measure the edge chipping resistance and flexural strength of the PICN and RNC materials and compare them with 2 commonly used feldspathic ceramic (FC) and leucite reinforced glass-ceramic (LRGC) CAD-CAM materials that share the same clinical indications. Material and methods PICN, RNC, FC, and LRGC material specimens were obtained by sectioning commercially available CAD-CAM blocks. Edge chipping test specimens (n=20/material) were adhesively attached to a resin substrate before testing. Edge chips were produced using a 120-degree, sharp, conical diamond indenter mounted on a universal testing machine and positioned 0.1 to 0.7 mm horizontally from the specimen’s edge. The chipping force was plotted against distance to the edge, and the data were fitted to linear and quadratic equations. One-way ANOVA determined intergroup differences (α=.05) in edge chipping toughness. Beam specimens (n=22/material) were tested for determining flexural strength using a 3-point bend test. Weibull statistics determined intergroup differences (α=.05). Flexural modulus and work of fracture were also calculated, and 1-way ANOVA determined intergroup differences (α=.05) Results Significant (PLRGC=FC\u3ePICN; flexural strength: RNC=LRGC\u3ePICN\u3eFC; flexural modulus: RNCLRGC=PICN\u3eFC. Conclusions The RNC material demonstrated superior performance for the mechanical properties tested compared with the other 3 materials