scholarly journals Healthcare Data Analysis Using Water Wave Optimization-Based Diagnostic Model

2021 ◽  
Vol 20 (No.4) ◽  
pp. 457-488
Author(s):  
Yugal Kumar ◽  
Arvinder Kaur
Keyword(s):  
Performance Indicators ◽  
Water Wave ◽  
Optimal Solution ◽  
Premature Convergence ◽  
Diagnostic Model ◽  
Accuracy Rate ◽  
Convergence Problem ◽  
Healthcare Data ◽  
Diagnosis Accuracy ◽  
Convergence Problems

This paper presents a new diagnostic model for various diseases. In the proposed diagnostic model, a water wave optimization (WWO) algorithm was implemented for improving the diagnosis accuracy. It was observed that the WWO algorithm suffered from the absence of global best information and premature convergence problems. Therefore in this work, some improvements were proposed to formulate the WWO algorithm as more promising and efficient. The global best information issue was addressed by using an improved solution search equation and the aim of this was to explore the global best optimal solution. Furthermore, a premature convergence problem was rectified by using a decay operator. These improvements were incorporated in the propagation and refraction phases of the WWO algorithm. The proposed algorithm was integrated into a diagnostic model for the analysis of healthcare data. The proposed algorithm aimed to improve the diagnosis accuracy of various diseases. The diverse disease datasets were considered for implementing the performance of the proposed diagnostic model based on accuracy and F-score performance indicators, while the existing techniques were regarded to compare the simulation results. The results confirmed that the WWO-based diagnostic model achieved a higher accuracy rate as compared to existing models/techniques with most disease/healthcare datasets. Therefore, it stated that the proposed diagnostic model is more promising and efficient for the diagnosis of different diseases.

Analyzing Healthcare Data Using Water Wave Optimization-Based Clustering Technique

2021 ◽  
Vol 10 (4) ◽  
pp. 38-57
Author(s):  
Arvinder Kaur ◽  
Yugal Kumar
Keyword(s):  
Medical Informatics ◽  
Performance Indicators ◽  
Water Wave ◽  
Disease Diagnosis ◽  
Diagnostic Model ◽  
Mutation Operator ◽  
Healthcare Data ◽  
Proposed Model ◽  
And Performance ◽  
Computational Intelligence Methods

The medical informatics field gets wide attention among the research community while developing a disease diagnosis expert system for useful and accurate predictions. However, accuracy is one of the major medical informatics concerns, especially for disease diagnosis. Many researchers focused on the disease diagnosis system through computational intelligence methods. Hence, this paper describes a new diagnostic model for analyzing healthcare data. The proposed diagnostic model consists of preprocessing, diagnosis, and performance evaluation phases. This model implements the water wave optimization (WWO) algorithm to analyze the healthcare data. Before integrating the WWO algorithm in the proposed model, two modifications are inculcated in WWO to make it more robust and efficient. These modifications are described as global information component and mutation operator. Several performance indicators are applied to assess the diagnostic model. The proposed model achieves better results than existing models and algorithms.

scholarly journals Identification of Skin Lesions by Using Single-Step Multiframe Detector

2021 ◽  
Vol 10 (1) ◽  
pp. 144
Author(s):  
Yu-Ping Hsiao ◽  
Chih-Wei Chiu ◽  
Chih-Wei Lu ◽  
Hong Thai Nguyen ◽  
Yu Sheng Tseng ◽  
...  
Keyword(s):  
Ground Truth ◽  
Skin Lesions ◽  
Single Step ◽  
Single Shot ◽  
Accuracy Rate ◽  
Tissue Slices ◽  
Image Detection ◽  
Sensitivity Rate ◽  
Diagnosis Accuracy ◽  
Artificial Intelligence Algorithm

An artificial intelligence algorithm to detect mycosis fungoides (MF), psoriasis (PSO), and atopic dermatitis (AD) is demonstrated. Results showed that 10 s was consumed by the single shot multibox detector (SSD) model to analyze 292 test images, among which 273 images were correctly detected. Verification of ground truth samples of this research come from pathological tissue slices and OCT analysis. The SSD diagnosis accuracy rate was 93%. The sensitivity values of the SSD model in diagnosing the skin lesions according to the symptoms of PSO, AD, MF, and normal were 96%, 80%, 94%, and 95%, and the corresponding precision were 96%, 86%, 98%, and 90%. The highest sensitivity rate was found in MF probably because of the spread of cancer cells in the skin and relatively large lesions of MF. Many differences were found in the accuracy between AD and the other diseases. The collected AD images were all in the elbow or arm and other joints, the area with AD was small, and the features were not obvious. Hence, the proposed SSD could be used to identify the four diseases by using skin image detection, but the diagnosis of AD was relatively poor.

scholarly journals Diagnosis and Analysis of Transabdominal and Intracavitary Ultrasound in Gynecological Acute Abdomen

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Huali Yang ◽  
Renying Wang ◽  
Liangchao Zhao ◽  
Jinhua Ye ◽  
Nengping Li ◽  
...  
Keyword(s):  
Acute Abdomen ◽  
Statistical Data ◽  
Abdominal Ultrasound ◽  
Accuracy Rate ◽  
Acute Pelvic Inflammatory Disease ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Effective Diagnosis ◽  
Ultrasound Group ◽  
Corpus Luteum Cyst

In order to explore the effective diagnosis method of gynecological acute abdomen, this paper takes hospital gynecological acute abdomen patients as samples and selects gynecological acute abdomen patients admitted to the hospital to be included in this study. They are divided into transabdominal ultrasound group, intracavitary ultrasound group, and combined group. Moreover, this paper uses mathematical statistics to carry out sample statistics. The statistical data mainly include ectopic pregnancy, torsion of ovarian tumor pedicle, acute suppurative salpingitis, torsion of fallopian tube, hemorrhagic salpingitis, acute pelvic inflammatory disease, rupture of corpus luteum cyst, and diagnosis accuracy rate. In addition, this paper compares the diagnostic accuracy of the abdominal ultrasound group, the intracavitary ultrasound group, and the combined group. The experimental research shows that the combined ultrasound diagnosis method can effectively improve the accuracy of the diagnosis of gynecological acute abdomen.

A Modified Reliability Index Approach for Reliability-Based Design Optimization

2009 ◽  
Author(s):  
Po Ting Lin ◽  
Hae Chang Gea ◽  
Yogesh Jaluria
Keyword(s):  
Reliability Index ◽  
Reliability Based Design Optimization ◽  
Convergence Problem ◽  
Analysis Problem ◽  
Numerical Examples ◽  
Reliability Based Design ◽  
Index Approach ◽  
Convergence Problems ◽  
Definition Of ◽  
Performance Constraint

RBDO problems have been intensively studied for many decades. Since Hasofer and Lind defined a measure of the second-moment reliability index, many RBDO methods utilizing the concept of reliability index have been introduced as the Reliability Index Approach (RIA). In the RIA, a reliability analysis problem is formulated to find the reliability index for each performance constraint and the solutions are used to evaluate the failure probability. However, the traditional RIA suffers from inefficiency and convergence problems. In this paper, we revisited the definition of the reliability index and revealed the convergence problem in the traditional RIA. Furthermore, a new definition of the reliability index is proposed to correct this problem and a modified Reliability Index Approach based on this definition is developed. Numerical examples using both the traditional RIA and the modified RIA are compared and discussed.

Optimal Reactive Power Dispatch Based on Mixed Bacterial Chemotaxis Algorithm

2014 ◽  
Vol 494-495 ◽  
pp. 1849-1852 ◽  
Author(s):  
Xiao Ying Zhang ◽  
Chen Li ◽  
Zhen Li
Keyword(s):  
Reactive Power ◽  
Power Optimization ◽  
Bacterial Chemotaxis ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Premature Convergence ◽  
Local Optimum ◽  
Reactive Power Optimization ◽  
Optimal Reactive Power Dispatch ◽  
Elite Strategy

Particle Swarm Optimization (PSO) algorithm converges fast but it is easy to fall into local optimum, and bacterial chemotaxis (BC) algorithm prevents premature convergence and prevents falling into local optimum, so a new mixed bacterial chemotaxis (MBC) algorithm is proposed by combining the PSO with BC. The novel algorithm is applied to reactive power optimization on power system. First the PSO is used to find best solution, then BC is used to find the optimal solution among the selected area of previous step, the reserving elite strategy is introduced to enhance the efficiency of the algorithm, and then the optimal solution is obtained. Through the comparison with PSO and BCC in the reactive power optimization of IEEE30-bus system, the results indicate that MBC not only prevents premature convergence to a large extent, but also keeps a more rapid convergence rate than PSO and BCC.

Convergence Problems and Energetic Regions in π Excited States of Certain Conjugated Molecules

1980 ◽  
Vol 35 (10) ◽  
pp. 1006-1012
Author(s):  
Myriam S. de Giambiagi ◽  
Mario Giambiagi ◽  
Henrique G. P. Lins de Barros
Keyword(s):  
Excited States ◽  
Variation Method ◽  
Bond Orders ◽  
Convergence Problem ◽  
Conjugated Molecules ◽  
Particular Solutions ◽  
Convergence Parameter ◽  
Convergence Problems

AbstractWhen calculating π bond orders of excited and superexcited states of conjugated molecules, difficulties arise in applying the variation method; besides, the convergence problems involved are well known. For pyridazine, chosen for discussion, 27 states are considered; the convergence problem is envisaged through two criteria in the choice of a parameter introduced in the compromise Hamiltonian. This convergence parameter is related to the variation method. There exist three particular solutions for bond orders, which divide the 27 states into energetical regions.

The Missile Fault Diagnosis Expert System Based on GA-BPNN

2011 ◽  
Vol 255-260 ◽  
pp. 2164-2168
Author(s):  
Jian Xiao Zou ◽  
Yao Zhang ◽  
Gang Zheng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Expert System ◽  
Bp Neural Network ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Diagnosis Accuracy ◽  
Simulation Results ◽  
Locally Optimal Solution

To improve the performance of fault diagnosis expert system based on ANN IN fields of convergence speed, locally optimal solution and the low accuracy, an missile fault diagnosis expert system based on GA-BPNN is proposed in this paper. The genetic algorithm (GA) is adopt to optimize the weight and threshold of matrix while BP neural network realizes the non-linear map relations between failure feature and failure cause. The simulation results indicate that the method proposed in this paper significantly increase the convergence speed and globally optimal solution of neural network, the fault diagnosis accuracy of expert system for a missile has been improved also.

scholarly journals Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wei Wei ◽  
Xu Yang
Keyword(s):  
Systematic Review ◽  
Data Structure ◽  
Linear Regression ◽  
Digestive Disease ◽  
Diagnostic Model ◽  
Noninvasive Diagnosis ◽  
Ann Model ◽  
Digestive Diseases ◽  
Diagnosis Accuracy ◽  
Diagnosis Model

Introduction. A Noninvasive diagnosis model for digestive diseases is the vital issue for the current clinical research. Our systematic review is aimed at demonstrating diagnosis accuracy between the BP-ANN algorithm and linear regression in digestive disease patients, including their activation function and data structure. Methods. We reported the systematic review according to the PRISMA guidelines. We searched related articles from seven electronic scholarly databases for comparison of the diagnosis accuracy focusing on BP-ANN and linear regression. The characteristics, patient number, input/output marker, diagnosis accuracy, and results/conclusions related to comparison were extracted independently based on inclusion criteria. Results. Nine articles met all the criteria and were enrolled in our review. Of those enrolled articles, the publishing year ranged from 1991 to 2017. The sample size ranged from 42 to 3222 digestive disease patients, and all of the patients showed comparable biomarkers between the BP-ANN algorithm and linear regression. According to our study, 8 literature demonstrated that the BP-ANN model is superior to linear regression in predicting the disease outcome based on AUROC results. One literature reported linear regression to be superior to BP-ANN for the early diagnosis of colorectal cancer. Conclusion. The BP-ANN algorithm and linear regression both had high capacity in fitting the diagnostic model and BP-ANN displayed more prediction accuracy for the noninvasive diagnosis model of digestive diseases. We compared the activation functions and data structure between BP-ANN and linear regression for fitting the diagnosis model, and the data suggested that BP-ANN was a comprehensive recommendation algorithm.

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