scholarly journals A SURVEY ON CLASSIFICATION OF LIVER TUMOUR FROM ABDOMINAL COMPUTED TOMOGRAPHY USING MACHINE LEARNING TECHNIQUES

2021 ◽  
pp. 148-156
Author(s):  
Gunasundari S ◽  
Swetha R
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Liver Disease ◽  
Liver Cancer ◽  
Diagnostic System ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Significant Area ◽  
System Benefit ◽  
Sensitivity Specificity

Pattern recognition is a significant area of research in medicine because many applications like diagnostic system benefit from it. The aim of this research is to analyze developments of liver cancer detection using machine learning techniques for liver disease. The study highlights how liver cancer diagnosis is assisted using machine learning with supervised, unsupervised and deep learning techniques. Several state of art techniques are compared based on performance measures such as accuracy, sensitivity, specificity. Finally, challenges are also highlighted for possible future work. KEYWORDS: Machine Learning, Liver, Liver disease, Computer Aided Diagnosis system, Liver Cancer, Computed Tomography

Machine Learning Techniques to Predict Software Defect

2014 ◽  
pp. 1422-1434 ◽  
Author(s):  
Ramakanta Mohanty ◽  
Vadlamani Ravi
Keyword(s):  
Machine Learning ◽  
Feature Subset Selection ◽  
Machine Learning Techniques ◽  
Group Method ◽  
Software Project ◽  
Feature Subset ◽  
Software Defects ◽  
Software Defect ◽  
Learning Techniques ◽  
Sensitivity Specificity

The past 10 years have seen the prediction of software defects proposed by many researchers using various metrics based on measurable aspects of source code entities (e.g. methods, classes, files or modules) and the social structure of software project in an effort to predict the software defects. However, these metrics could not predict very high accuracies in terms of sensitivity, specificity and accuracy. In this chapter, we propose the use of machine learning techniques to predict software defects. The effectiveness of all these techniques is demonstrated on ten datasets taken from literature. Based on an experiment, it is observed that PNN outperformed all other techniques in terms of accuracy and sensitivity in all the software defects datasets followed by CART and Group Method of data handling. We also performed feature selection by t-statistics based approach for selecting feature subsets across different folds for a given technique and followed by the feature subset selection. By taking the most important variables, we invoked the classifiers again and observed that PNN outperformed other classifiers in terms of sensitivity and accuracy. Moreover, the set of ‘if- then rules yielded by J48 and CART can be used as an expert system for prediction of software defects.

scholarly journals An Empirical Study of AML Approach for Credit Card Fraud Detection–Financial Transactions

2019 ◽  
Vol 14 (6) ◽  
pp. 670-690 ◽  
Author(s):  
Ajeet Singh ◽  
Anurag Jain
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Evaluation Criteria ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Credit Card Fraud ◽  
Detection Techniques ◽  
Digital World ◽  
Learning Techniques ◽  
Sensitivity Specificity

Credit card fraud is one of the flip sides of the digital world, where transactions are made without the knowledge of the genuine user. Based on the study of various papers published between 1994 and 2018 on credit card fraud, the following objectives are achieved: the various types of credit card frauds has identified and to detect automatically these frauds, an adaptive machine learning techniques (AMLTs) has studied and also their pros and cons has summarized. The various dataset are used in the literature has studied and categorized into the real and synthesized datasets.The performance matrices and evaluation criteria have summarized which has used to evaluate the fraud detection system.This study has also covered the deep analysis and comparison of the performance (i.e sensitivity, specificity, and accuracy) of existing machine learning techniques in the credit card fraud detection area.The findings of this study clearly show that supervised learning, card-not-present fraud, skimming fraud, and website cloning method has been used more frequently.This Study helps to new researchers by discussing the limitation of existing fraud detection techniques and providing helpful directions of research in the credit card fraud detection field.

scholarly journals An interpretable classification method for predicting drug resistance in M. tuberculosis

2020 ◽  
Author(s):  
Hooman Zabeti ◽  
Nick Dexter ◽  
Amir Hosein Safari ◽  
Nafiseh Sedaghat ◽  
Maxwell Libbrecht ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Resistance ◽  
Compressed Sensing ◽  
Predictive Accuracy ◽  
Group Testing ◽  
Predictive Performance ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Sensitivity Specificity ◽  
Interpretable Classification

AbstractMotivationThe prediction of drug resistance and the identification of its mechanisms in bacteria such as Mycobacterium tuberculosis, the etiological agent of tuberculosis, is a challenging problem. Modern methods based on testing against a catalogue of previously identified mutations often yield poor predictive performance. On the other hand, machine learning techniques have demonstrated high predictive accuracy, but many of them lack interpretability to aid in identifying specific mutations which lead to resistance. We propose a novel technique, inspired by the group testing problem and Boolean compressed sensing, which yields highly accurate predictions and interpretable results at the same time.ResultsWe develop a modified version of the Boolean compressed sensing problem for identifying drug resistance, and implement its formulation as an integer linear program. This allows us to characterize the predictive accuracy of the technique and select an appropriate metric to optimize. A simple adaptation of the problem also allows us to quantify the sensitivity-specificity trade-off of our model under different regimes. We test the predictive accuracy of our approach on a variety of commonly used antibiotics in treating tuberculosis and find that it has accuracy comparable to that of standard machine learning models and points to several genes with previously identified association to drug resistance.Availabilityhttps://github.com/hoomanzabeti/[email protected]

Image recognition of COVID-19 using DarkCovidNet architecture based on convolutional neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pankaj Kumar ◽  
Bhavna Bajpai ◽  
Deepak Omprakash Gupta ◽  
Dinesh C. Jain ◽  
S. Vimal
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computed Tomography ◽  
Convolutional Neural Network ◽  
Design Methodology ◽  
Machine Learning Techniques ◽  
Computed Tomography Scan ◽  
Content Type ◽  
Learning Techniques ◽  
Tomography Scan

Purpose The purpose of this study/paper To focus on finding COVID-19 with the help of DarkCovidNet architecture on patient images. Design/methodology/approach We used machine learning techniques with convolutional neural network. Findings Detecting COVID-19 symptoms from patient CT scan images. Originality/value This paper contains a new architecture for detecting COVID-19 symptoms from patient computed tomography scan images.

scholarly journals Prediction of Liver Disease using Machine Learning Algorithms

2020 ◽  
Vol 9 (9) ◽  
pp. 57-59
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Good Accuracy ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Medical Field ◽  
Research Papers ◽  
Use Of Data ◽  
Learning Techniques

There are different machine learning techniques widely used in medical field to diagnosis and to predict the liver disease. To endorse the analysis of high and multi dimensional data in health care industry we have reviewed various research papers in which we have focused on various Data mining methods for making use of data in regard to this we come out with, assessment for chosen research papers. Hence, the Objective of this study is to improve the diagnosis and Prediction of the liver disease with the machine learning algorithms. In our paper we suggested that hybrid of Decision Tree and Navie Bayes can give better result with good accuracy.

scholarly journals Liver Disease Prediction using Machine-Learning Algorithms

2019 ◽  
Vol 8 (6) ◽  
pp. 2532-2534
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Learning Techniques ◽  
To Come ◽  
Developing Pattern

machine learning is a part of man-made consciousness that utilizes an assortment of measurable, probabilistic and enhancement methods that enables PCs to "learn" from past precedents and to identify hard-to-recognize designs from huge, boisterous or complex informational indexes. This capacity is especially appropriate to restorative applications, particularly those that rely upon complex proteomic and genomic estimations. Therefore, machine learning is every now and again utilized in disease conclusion and discovery. All the more as of late machine learning has been connected to disease guess and forecast. This last mentioned approach is especially intriguing as it is a piece of a developing pattern towards customized, prescient drug. In collecting this audit we led a wide overview of the distinctive sorts of machine learning techniques being utilized, the kinds of information being coordinated and the execution of these techniques in growth forecast and visualization. Various distributed examinations additionally appear to come up short on a fitting level of approval or testing. Among the better composed and approved investigations unmistakably machine learning techniques can be utilized to generously (15-25%) enhance the precision of foreseeing disease powerlessness, repeat what's more, mortality. At a more major level, it is additionally apparent that machine learning is likewise enhancing our fundamental comprehension of disease improvement and movement.

scholarly journals Analysis of Machine Learning Techniques for Breast Cancer Prediction

2021 ◽  
Vol 11 (1) ◽  
pp. 79-83
Author(s):  
Mrs N. Vanitha ◽  
R. Srimathi ◽  
J Haritha
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Indian Women ◽  
Cancer Prediction ◽  
Risk Of Death ◽  
Learning Techniques ◽  
Learning Technique ◽  
Sensitivity Specificity

The most frequently happening cancer among Indian women is breast cancer, which is the second most exposed cancer in the world. Here is a chance of fifty percent for fatality in a case as one of two women diagnosed with breast cancer die in the cases of Indian women.  With the rapid population growth, the risk of death incurred by breast cancer is rising exponentially. [2] Breast cancer is the second most severe cancer among all of the cancers already unveiled. A machine learning technique discovers illness which helps clinical staffs in sickness analysis and offers dependable, powerful, and quick reaction just as diminishes the danger of death. In this paper, we look at five administered AI methods named Support vector machine (SVM), K-closest neighbours, irregular woodlands, fake/ Artificial neural organizations (ANNs). The performance of the study is measured with respect to accuracy, sensitivity, specificity, precision, negative predictive value. Furthermore, these strategies were evaluated on exactness review region under bend and beneficiary working trademark bend. At last in this paper we analysed some of different papers to find how they are predicted and what are all the techniques they were used and finally we study the complete research of machine learning techniques for breast cancer.

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