897 research outputs found

    Effective Prostate Cancer Detection using Enhanced Particle Swarm Optimization Algorithm with Random Forest on the Microarray Data

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    Prostate Cancer (PC) is the leading cause of mortality among males, therefore an effective system is required for identifying the sensitive bio-markers for early recognition. The objective of the research is to find the potential bio-markers for characterizing the dissimilar types of PC. In this article, the PC-related genes are acquired from the Gene Expression Omnibus (GEO) database. Then, gene selection is accomplished using enhanced Particle Swarm Optimization (PSO) to select the active genes, which are related to the PC. In the enhanced PSO algorithm, the interval-newton approach is included to keep the search space adaptive by varying the swarm diversity that helps to perform the local search significantly. The selected active genes are fed to the random forest classifier for the classification of PC (high and low-risk). As seen in the experimental investigation, the proposed model achieved an overall classification accuracy of 96.71%, which is better compared to the traditional models like naĂŻve Bayes, support vector machine and neural network

    Gene selection and classification in autism gene expression data

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    Autism spectrum disorders (ASD) are neurodevelopmental disorders that are currently diagnosed on the basis of abnormal stereotyped behaviour as well as observable deficits in communication and social functioning. Although a variety of candidate genes have been attributed to the disorder, no single gene is applicable to more than 1–2% of the general ASD population. Despite extensive efforts, definitive genes that contribute to autism susceptibility have yet to be identified. The major problems in dealing with the gene expression dataset of autism include the presence of limited number of samples and large noises due to errors of experimental measurements and natural variation. In this study, a systematic combination of three important filters, namely t-test (TT), Wilcoxon Rank Sum (WRS) and Feature Correlation (COR) are applied along with efficient wrapper algorithm based on geometric binary particle swarm optimization-support vector machine (GBPSO-SVM), aiming at selecting and classifying the most attributed genes of autism. A new approach based on the criterion of median ratio, mean ratio and variance deviations is also applied to reduce the initial dataset prior to its involvement. Results showed that the most discriminative genes that were identified in the first and last selection steps concluded the presence of a repetitive gene (CAPS2), which was assigned as the most ASD risk gene. The fused result of genes subset that were selected by the GBPSO-SVM algorithm increased the classification accuracy to about 92.10%, which is higher than those reported in literature for the same autism dataset. Noticeably, the application of ensemble using random forest (RF) showed better performance compared to that of previous studies. However, the ensemble approach based on the employment of SVM as an integrator of the fused genes from the output branches of GBPSO-SVM outperformed the RF integrator. The overall improvement was ascribed to the selection strategies that were taken to reduce the dataset and the utilization of efficient wrapper based GBPSO-SVM algorithm

    En-PaFlower: An Ensemble Approach using PSO and Flower Pollination Algorithm for Cancer Diagnosis

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    Machine learning now is used across many sectors and provides consistently precise predictions. The machine learning system is able to learn effectively because the training dataset contains examples of previously completed tasks. After learning how to process the necessary data, researchers have proven that machine learning algorithms can carry out the whole work autonomously. In recent years, cancer has become a major cause of the worldwide increase in mortality. Therefore, early detection of cancer improves the chance of a complete recovery, and Machine Learning (ML) plays a significant role in this perspective. Cancer diagnostic and prognosis microarray dataset is available with the biopsy dataset. Because of its importance in making diagnoses and classifying cancer diseases, the microarray data represents a massive amount. It may be challenging to do an analysis on a large number of datasets, though. As a result, feature selection is crucial, and machine learning provides classification techniques. These algorithms choose the relevant features that help build a more precise categorization model. Accurately classifying diseases is facilitated as a result, which aids in disease prevention. This work aims to synthesize existing knowledge on cancer diagnosis using machine learning techniques into a compact report.  Current research work aims to propose an ensemble-based machine learning model En-PaFlower using Particle Swarm Optimization (PSO) as the feature selection algorithm, Flower Pollination algorithm (FPA) as the optimization algorithm with the majority voting algorithm. Finally, the performance of the proposed algorithm is evaluated over three different types of cancer disease datasets with accuracy, precision, recall, specificity, and F-1 Score etc as the evaluation parameters. The empirical analysis shows that the proposed methodology shows highest accuracy as 95.65%

    Computational models and approaches for lung cancer diagnosis

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    The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, the aim of this study is to developed novel lung cancer diagnostic models. New algorithms are proposed to analyse the biological data and extract knowledge that assists in achieving accurate diagnosis results

    Enhanced Ai-Based Machine Learning Model for an Accurate Segmentation and Classification Methods

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    Phone Laser Scanner becomes the versatile sensor module that is premised on Lamp Identification and Spanning methodology and is used in a spectrum of uses. There are several prior editorials in the literary works that concentrate on the implementations or attributes of these processes; even so, evaluations of all those inventive computational techniques reported in the literature have not even been performed in the required thickness. At ToAT that finish, we examine and summarize the latest advances in Artificial Intelligence based machine learning data processing approaches such as extracting features, fragmentation, machine vision, and categorization. In this survey, we have reviewed total 48 papers based on an enhanced AI based machine learning model for accurate classification and segmentation methods. Here, we have reviewed the sections on segmentation and classification of images based on machine learning models

    Investigation of diagnostic value of artificial intelligence systems in the diagnosis of breast cancer based on histopathological images using Meta-MUMS DTA tool

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    ORIGINAL ARTICLES Epidemiology Biostatistics and Public Health - 2020, Volume 17, Number 2Investigation of diagnostic value of artificial intelligence systems in the diagnosis of breast cancer based on histopathological images using Meta-MUMS DTA toolInvestigation of diagnostic value of artificialintelligence systems in the diagnosis of breastcancer based on histopathological imagesusing Meta-MUMS DTA toolABSTRACTBackground: Various artificial intelligence systems are available for diagnosing breast cancer based onhistopathological images. Assessing the performance of existing methodologies for breast cancer diagnosis is vital.Methods: The SCOPUS database has been searched for studies up to December 15, 2018. We extracted the data,including "true positive," "true negative," "false positive," and "false negative". The pooled sensitivity, pooled specificity,positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, area under the curve of summary receiveroperating characteristic curve were useful in assessing the diagnostic accuracy. Egger's test, Deeks' funnel plot, SVE(Smoothed Variance regression model based on Egger’s test), SVT (Smoothed Variance regression model based onThompson’s method), and trim and fill methodologies were essential tests for publication bias identification.Results: Three studies with eight approaches from thirty-seven articles were found eligible for further analysis. Asensitivity of 0.95, a specificity of 0.78, a PLR of 7525, an NLR of 0.06, a DOR of 88.15, and an AUC of 0.953showed high significant heterogeneity; however, the reason was not the threshold effect. The publication bias wasdetected by SVE, SVT, and trim and fill analysis.Conclusion: The artificial intelligent (AI) systems play a pivotal role in the diagnosis of breast cancer usinghistopathological cell images and are important decision-makers for pathologists. The analyses revealed that theoverall accuracy of AI systems is promising for breast cancer; however, the pooled specificity is lower than pooledsensitivity. Moreover, the approval of the results awaits conducting randomized clinical trials with sufficient dat

    Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

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    Multi-Verse Optimization (MVO) is one of the newest meta-heuristic optimization algorithms which imitates the theory of Multi-Verse in Physics and resembles the interaction among the various universes. In problem domains like feature selection, the solutions are often constrained to the binary values viz. 0 and 1. With regard to this, in this paper, binary versions of MVO algorithm have been proposed with two prime aims: firstly, to remove redundant and irrelevant features from the dataset and secondly, to achieve better classification accuracy. The proposed binary versions use the concept of transformation functions for the mapping of a continuous version of the MVO algorithm to its binary versions. For carrying out the experiments, 21 diverse datasets have been used to compare the Binary MVO (BMVO) with some binary versions of existing metaheuristic algorithms. It has been observed that the proposed BMVO approaches have outperformed in terms of a number of features selected and the accuracy of the classification process

    Gene selection for cancer classification with the help of bees

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