Fuzzy based uncertainty modeling of Cancer Diagnosis System
01 Dec 2017-
TL;DR: The method developed for the diagnosis of cancer is to detect, whether it is benign or malignant using the artificial neural network, and a neuro-fuzzy system is developed which is based on the mamdani model.
Abstract: Recent advances in the field of artificial intelligence and machine learning have led to the emergence of expert systems for medical applications. In this paper a machine learning approach is implemented to diagnose cancer. The method developed for the diagnosis of cancer is to detect, whether it is benign or malignant using the artificial neural network. The network is trained using back-propagation algorithm. In the further test the membership values are calculated using fuzzy-c-means algorithm. They depict the possibility for a benign to turn into malignant. Then a neuro-fuzzy system is developed which is based on the mamdani model. The rules are set up using the pruned and un-pruned decision tree to identify the magnitude of cancer.
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TL;DR: Based on the comparison of the system developed in this work against the previous works that used fuzzy neural network or other applied artificial intelligence techniques on the same dataset for diagnosis of breast cancer using the same datasets, the system stands to be the best in terms of accuracy, sensitivity, and specificity.
Abstract: Breast cancer is one of the common malignancies among females in Saudi Arabia and has also been ranked as the one most prevalent and the number two killer disease in the country. However, the clinical diagnosis process of any disease such as breast cancer, coronary artery diseases, diabetes, COVID-19, among others, is often associated with uncertainty due to the complexity and fuzziness of the process. In this work, a fuzzy neural network expert system with an improved gini index random forest-based feature importance measure algorithm for early diagnosis of breast cancer in Saudi Arabia was proposed to address the uncertainty and ambiguity associated with the diagnosis of breast cancer and also the heavier burden on the overlay of the network nodes of the fuzzy neural network system that often happens due to insignificant features that are used to predict or diagnose the disease. An Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm was used to select the five fittest features of the diagnostic wisconsin breast cancer database out of the 32 features of the dataset. The logistic regression, support vector machine, k-nearest neighbor, random forest, and gaussian naïve bayes learning algorithms were used to develop two sets of classification models. Hence, the classification models with full features (32) and models with the 5 fittest features. The two sets of classification models were evaluated, and the results of the evaluation were compared. The result of the comparison shows that the models with the selected fittest features outperformed their counterparts with full features in terms of accuracy, sensitivity, and sensitivity. Therefore, a fuzzy neural network based expert system was developed with the five selected fittest features and the system achieved 99.33% accuracy, 99.41% sensitivity, and 99.24% specificity. Moreover, based on the comparison of the system developed in this work against the previous works that used fuzzy neural network or other applied artificial intelligence techniques on the same dataset for diagnosis of breast cancer using the same dataset, the system stands to be the best in terms of accuracy, sensitivity, and specificity, respectively. The z test was also conducted, and the test result shows that there is significant accuracy achieved by the system for early diagnosis of breast cancer.
28 citations
TL;DR: Artificial intelligence techniques have been applied to EHR-type data to facilitate early diagnosis of cancer, but their use in primary care settings is still at an early stage of maturity.
Abstract: Background: More than 17 million people worldwide, including 360,000 people in the United Kingdom, were diagnosed with cancer in 2018. Cancer prognosis and disease burden are highly dependent on the disease stage at diagnosis. Most people diagnosed with cancer first present in primary care settings, where improved assessment of the (often vague) presenting symptoms of cancer could lead to earlier detection and improved outcomes for patients. There is accumulating evidence that artificial intelligence (AI) can assist clinicians in making better clinical decisions in some areas of health care.
Objective: This study aimed to systematically review AI techniques that may facilitate earlier diagnosis of cancer and could be applied to primary care electronic health record (EHR) data. The quality of the evidence, the phase of development the AI techniques have reached, the gaps that exist in the evidence, and the potential for use in primary care were evaluated.
Methods: We searched MEDLINE, Embase, SCOPUS, and Web of Science databases from January 01, 2000, to June 11, 2019, and included all studies providing evidence for the accuracy or effectiveness of applying AI techniques for the early detection of cancer, which may be applicable to primary care EHRs. We included all study designs in all settings and languages. These searches were extended through a scoping review of AI-based commercial technologies. The main outcomes assessed were measures of diagnostic accuracy for cancer.
Results: We identified 10,456 studies; 16 studies met the inclusion criteria, representing the data of 3,862,910 patients. A total of 13 studies described the initial development and testing of AI algorithms, and 3 studies described the validation of an AI algorithm in independent data sets. One study was based on prospectively collected data; only 3 studies were based on primary care data. We found no data on implementation barriers or cost-effectiveness. Risk of bias assessment highlighted a wide range of study quality. The additional scoping review of commercial AI technologies identified 21 technologies, only 1 meeting our inclusion criteria. Meta-analysis was not undertaken because of the heterogeneity of AI modalities, data set characteristics, and outcome measures.
Conclusions: AI techniques have been applied to EHR-type data to facilitate early diagnosis of cancer, but their use in primary care settings is still at an early stage of maturity. Further evidence is needed on their performance using primary care data, implementation barriers, and cost-effectiveness before widespread adoption into routine primary care clinical practice can be recommended.
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22 citations
Cites methods from "Fuzzy based uncertainty modeling of..."
...Full details of our review question, search strategy, inclusion or exclusion criteria, and data extraction methodology are described in Multimedia Appendices 1 [1-5,7-9,11-13,34-38] and 2, and the full list of excluded studies is provided in Multimedia Appendix 3 [34,39-114]....
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30 Jul 2019
TL;DR: The basis of worldwide cancer impact is provided, methodological study with discussion, attributes and parametric impact, gaps analyzed, and the suggested computational solutions are provided.
Abstract: In this paper approaches for cancer prediction through computational measures has been discussed and analyzed. This paper provides the basis of worldwide cancer impact, methodological study with discussion, attributes and parametric impact, gaps analyzed, and the suggested computational solutions. This paper also explores the impact and the association measures of the influencing factors. The methods covered in this study are from data mining and optimization. The latest trends in the methods used and applicability have been discussed with the gaps.
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Book•
14 Sep 1984
TL;DR: In this article, the distribution of the Mean Vector and the Covariance Matrix and the Generalized T2-Statistic is analyzed. But the distribution is not shown to be independent of sets of Variates.
Abstract: Preface to the Third Edition.Preface to the Second Edition.Preface to the First Edition.1. Introduction.2. The Multivariate Normal Distribution.3. Estimation of the Mean Vector and the Covariance Matrix.4. The Distributions and Uses of Sample Correlation Coefficients.5. The Generalized T2-Statistic.6. Classification of Observations.7. The Distribution of the Sample Covariance Matrix and the Sample Generalized Variance.8. Testing the General Linear Hypothesis: Multivariate Analysis of Variance9. Testing Independence of Sets of Variates.10. Testing Hypotheses of Equality of Covariance Matrices and Equality of Mean Vectors and Covariance Matrices.11. Principal Components.12. Cononical Correlations and Cononical Variables.13. The Distributions of Characteristic Roots and Vectors.14. Factor Analysis.15. Pattern of Dependence Graphical Models.Appendix A: Matrix Theory.Appendix B: Tables.References.Index.
9,693 citations
01 Jan 2016
TL;DR: The introduction to multivariate statistical analysis is universally compatible with any devices to read, and will help you to cope with some harmful bugs inside their desktop computer.
Abstract: Thank you for downloading introduction to multivariate statistical analysis. Maybe you have knowledge that, people have look hundreds times for their favorite books like this introduction to multivariate statistical analysis, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some harmful bugs inside their desktop computer. introduction to multivariate statistical analysis is available in our digital library an online access to it is set as public so you can get it instantly. Our books collection hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the introduction to multivariate statistical analysis is universally compatible with any devices to read.
3,304 citations
TL;DR: An automated system for detecting and classifying particular types of tumors in digitized mammograms is described, which uses a classification hierarchy to identify benign and malignant tumors.
Abstract: An automated system for detecting and classifying particular types of tumors in digitized mammograms is described The analysis of mammograms is performed in two stages First, the system identifies pixel groupings that may correspond to tumors Next, detected pixel groupings are subjected to classification The essence of the first processing stage is multiresolution image processing based on fuzzy pyramid linking The second stage uses a classification hierarchy to identify benign and malignant tumors Each level of the hierarchy uses deterministic or Bayes classifiers and a particular measurement The measurements pertain to shape and intensity characteristics of particular types of tumors The classification hierarchy is organized in such a way that the simplest measurements are used at the top, with the system stepping through the hierarchy only when it cannot classify the detected pixel groupings with certainty >
303 citations
TL;DR: In the management of suspicious nonpalpable mammographic findings, the rate of carcinoma for lesions at biopsy can approximate 40%.
Abstract: Carcinoma was found in 30% (119 of 400) of biopsy specimens obtained for mammographically suspicious but nonpalpable findings. The authors reviewed the mammograms of these 400 cases without knowledge of the biopsy results and placed each examination into one of four groups based on their suspicion for carcinoma: minimal (n = 82), slight (n = 91), moderate (n = 174), and high (n = 53). In 127 cases, mammograms showed either minimally suspicious calcifications (n = 33) or minimally (n = 49) or slightly (n = 45) suspicious masses. A 4.7% (six of 127) rate of carcinoma was found in these groups; five of the six cancers were in situ. Had follow-up mammography been done rather than biopsy for these 127 less suspicious lesions, it is probable that the delay in diagnosis would not have altered overall prognosis. In the remaining 273 patients, the positive predictive value of mammography for carcinoma would have risen from 30% (119 of 400) to 41% (113 of 273). The authors conclude that in the management of suspici...
290 citations
TL;DR: Two novel CAD approaches that both emphasize an intelligible decision process to predict breast biopsy outcomes from BI-RADS findings have the potential to reduce the number of unnecessary breast biopsies in clinical practice.
Abstract: Mammography is the most effective method for breast cancer screening available today. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis (CAD) systems have been proposed in the last several years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short term follow-up examination instead. We present two novel CAD approaches that both emphasize an intelligible decision process to predict breast biopsy outcomes from BI-RADS findings. An intelligible reasoning process is an important requirement for the acceptance of CAD systems by physicians. The first approach induces a global model based on decison-tree learning. The second approach is based on case-based reasoning and applies an entropic similarity measure. We have evaluated the performance of both CAD approaches on two large publicly available mammography reference databases using receiver operating characteristic (ROC) analysis, bootstrap sampling, and the ANOVA statistical significance test. Both approaches outperform the diagnosis decisions of the physicians. Hence, both systems have the potential to reduce the number of unnecessary breast biopsies in clinical practice. A comparison of the performance of the proposed decision tree and CBR approaches with a state of the art approach based on artificial neural networks (ANN) shows that the CBR approach performs slightly better than the ANN approach, which in turn results in slightly better performance than the decision-tree approach. The differences are statistically significant (p value < 0.001). On 2100 masses extracted from the DDSM database, the CRB approach for example resulted in an area under the ROC curve of A(z) = 0.89 +/- 0.01, the decision-tree approach in A(z) = 0.87 +/- 0.01, and the ANN approach in A(z) = 0.88 +/- 0.01.
230 citations