scholarly journals Different Perspective of Machine Learning Technique to Better Predict Breast Cancer Survival

2020 ◽  
Author(s):  
Kaushal Kumar ◽  
Vijay Vikram Singh ◽  
Ramakrishna Ramaswamy
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Survival ◽  
Malignant Tumors ◽  
Machine Learning Algorithms ◽  
Training Algorithm ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Genetic Algorithm Procedure ◽  
Viable Model

AbstractMachine learning (ML) plays a key job in the guide of cancer diagnosis and identification. The researcher has implemented different algorithms of ML for the prediction of breast cancer. Some researchers recommend their algorithms are more accurate, faster, and easier than others. My study relies on recently developed machine learning algorithms like genetic algorithms and deep belief nets. I’m interested to build a framework to precisely separate among benign and malignant tumors. We’ve optimized the training algorithm. During this unique circumstance, we applied the genetic algorithm procedure to settle on the main genuine highlights and perfect boundary estimations of the AI classifiers. The examinations rely upon affectability, cross-validation, precision, and ROC curve. Among all the varying kinds of classifiers used in this paper genetic programming is the premier viable model for highlight determination and classifier.

scholarly journals Computer Vision Based Detection and Quantification of Extraneous Water in Raw Milk

2021 ◽  
Author(s):  
Bezuayehu Gutema Asefa ◽  
Legesse Hagos ◽  
Tamirat Kore ◽  
Shimelis Admassu Emire
Keyword(s):  
Machine Learning ◽  
Digital Image Analysis ◽  
Raw Milk ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Machine Learning Technique ◽  
Milk Adulteration ◽  
Learning Technique ◽  
Total Accuracy

Abstract A rapid method based on digital image analysis and machine learning technique is proposed for the detection of milk adulteration with water. Several machine learning algorithms were compared, and SVM performed best with 89.48 % of total accuracy and 95.10 % precision. An increase in the classification performance was observed in extreme classes. Better quantitative determination of the extraneous water was achieved using SVMR with R2(CV) and R2(P) of 0.65 and 0.71 respectively. The proposed technique can be used to screen raw milk based on the level of added extraneous water without the necessity of any additional reagent.

scholarly journals Detection of Breast Cancer in the Bio Medical Image using Supervised Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 1140-1142
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Extraction ◽  
Medical Image ◽  
Image Sensors ◽  
Supervised Machine Learning ◽  
Machine Learning Technique ◽  
Biomedical Image ◽  
Learning Technique ◽  
Learning Concept

The detection of disease for diagnosis in the medical image becomes easier, when the machine learning concept is used. In this article, the Breast cancer is detected from the biomedical image using supervised machine learning technique. The bio medical image is acquired from the image sensors and that acquired image is pre processed for further processing. From that pre-processed image, the object detection is performed. The next processes are segmentation and feature extraction. Finally, the supervised learning is implemented in the image for image classification. With the help of machine learning, the different cases of cells are also detected, differentiated and spotted for the further diagnosis

Breast cancer prediction using an optimal machine learning technique for next generation sequences

2021 ◽  
pp. 1063293X2199180
Author(s):  
Babymol Kurian ◽  
VL Jyothi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Decision Tree ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Next Generation ◽  
Machine Learning Technique ◽  
Cancer Prediction ◽  
Learning Technique

A wide reach on cancer prediction and detection using Next Generation Sequencing (NGS) by the application of artificial intelligence is highly appreciated in the current scenario of the medical field. Next generation sequences were extracted from NCBI (National Centre for Biotechnology Information) gene repository. Sequences of normal Homo sapiens (Class 1), BRCA1 (Class 2) and BRCA2 (Class 3) were extracted for Machine Learning (ML) purpose. The total volume of datasets extracted for the process were 1580 in number under four categories of 50, 100, 150 and 200 sequences. The breast cancer prediction process was carried out in three major steps such as feature extraction, machine learning classification and performance evaluation. The features were extracted with sequences as input. Ten features of DNA sequences such as ORF (Open Reading Frame) count, individual nucleobase average count of A, T, C, G, AT and GC-content, AT/GC composition, G-quadruplex occurrence, MR (Mutation Rate) were extracted from three types of sequences for the classification process. The sequence type was also included as a target variable to the feature set with values 0, 1 and 2 for classes 1, 2 and 3 respectively. Nine various supervised machine learning techniques like LR (Logistic Regression statistical model), LDA (Linear Discriminant analysis model), k-NN (k nearest neighbours’ algorithm), DT (Decision tree technique), NB (Naive Bayes classifier), SVM (Support-Vector Machine algorithm), RF (Random Forest learning algorithm), AdaBoost (AB) and Gradient Boosting (GB) were employed on four various categories of datasets. Of all supervised models, decision tree machine learning technique performed most with maximum accuracy in classification of 94.03%. Classification model performance was evaluated using precision, recall, F1-score and support values wherein F1-score was most similar to the classification accuracy.

scholarly journals Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Fatin Nabihah Jais ◽  
Mohd Zulfaezal Che Azemin ◽  
Mohd Radzi Hilmi ◽  
Mohd Izzuddin Mohd Tamrin ◽  
Khairidzan Mohd Kamal
Keyword(s):  
Machine Learning ◽  
Visual Acuity ◽  
Visual Impairment ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Machine Learning Technique ◽  
Corrected Visual Acuity ◽  
Learning Technique ◽  
Best Corrected Visual Acuity

Introduction. Early detection of visual symptoms in pterygium patients is crucial as the progression of the disease can cause visual disruption and contribute to visual impairment. Best-corrected visual acuity (BCVA) and corneal astigmatism influence the degree of visual impairment due to direct invasion of fibrovascular tissue into the cornea. However, there were different characteristics of pterygium used to evaluate the severity of visual impairment, including fleshiness, size, length, and redness. The innovation of machine learning technology in visual science may contribute to developing a highly accurate predictive analytics model of BCVA outcomes in postsurgery pterygium patients. Aim. To produce an accurate model of BCVA changes of postpterygium surgery according to its morphological characteristics by using the machine learning technique. Methodology. A retrospective of the secondary dataset of 93 samples of pterygium patients with different pterygium attributes was used and imported into four different machine learning algorithms in RapidMiner software to predict the improvement of BCVA after pterygium surgery. Results. The performance of four machine learning techniques were evaluated, and it showed the support vector machine (SVM) model had the highest average accuracy (94.44% ± 5.86%), specificity (100%), and sensitivity (92.14% ± 8.33%). Conclusion. Machine learning algorithms can produce a highly accurate postsurgery classification model of BCVA changes using pterygium characteristics.

scholarly journals KiDS-SQuaD

2019 ◽  
Vol 632 ◽  
pp. A56 ◽  
Author(s):  
Vladislav Khramtsov ◽  
Alexey Sergeyev ◽  
Chiara Spiniello ◽  
Crescenzo Tortora ◽  
Nicola R. Napolitano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Input Data ◽  
Training Sample ◽  
Machine Learning Algorithms ◽  
Machine Learning Method ◽  
Gravitational Lenses ◽  
Machine Learning Technique ◽  
Quantitative Estimates ◽  
Starting Point ◽  
Learning Technique

Context. The KiDS Strongly lensed QUAsar Detection project (KiDS-SQuaD) is aimed at finding as many previously undiscovered gravitational lensed quasars as possible in the Kilo Degree Survey. This is the second paper of this series where we present a new, automatic object-classification method based on the machine learning technique. Aims. The main goal of this paper is to build a catalogue of bright extragalactic objects (galaxies and quasars) from the KiDS Data Release 4, with minimum stellar contamination and preserving the completeness as much as possible. We show here that this catalogue represents the perfect starting point to search for reliable gravitationally lensed quasar candidates. Methods. After testing some of the most used machine learning algorithms, decision-tree-based classifiers, we decided to use CatBoost, which was specifically trained with the aim of creating a sample of extragalactic sources that is as clean of stars as possible. We discuss the input data, define the training sample for the classifier, give quantitative estimates of its performances, and finally describe the validation results with Gaia DR2, AllWISE, and GAMA catalogues. Results. We built and made available to the scientific community the KiDS Bright EXtraGalactic Objects catalogue (KiDS-BEXGO), specifically created to find gravitational lenses but applicable to a wide number of scientific purposes. The KiDS-BEXGO catalogue is made of ≈6 million sources classified as quasars (≈200 000) and galaxies (≈5.7 M) up to r <  22m. To demonstrate the potential of the catalogue in the search for strongly lensed quasars, we selected ≈950 “Multiplets”: close pairs of quasars or galaxies surrounded by at least one quasar. We present cutouts and coordinates of the 12 most reliable gravitationally lensed quasar candidates. We showed that employing a machine learning method decreases the stellar contaminants within the gravitationally lensed candidates, comparing the current results to the previous ones, presented in the first paper from this series. Conclusions. Our work presents the first comprehensive identification of bright extragalactic objects in KiDS DR4 data, which is, for us, the first necessary step towards finding strong gravitational lenses in wide-sky photometric surveys, but has also many other more general astrophysical applications.

Beatmungsassoziierte Pneumonie: Elektronische Nase und künstliche Intelligenz als Potenzial für die Zukunft

2020 ◽  
Vol 8 (5) ◽  
pp. 254-255
Author(s):  
Johannes Knoch
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Predictive Value ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
The Mean

Background: Ventilator-associated pneumonia (VAP) is a significant cause of mortality in the intensive care unit. Early diagnosis of VAP is important to provide appropriate treatment and reduce mortality. Developing a noninvasive and highly accurate diagnostic method is important. The invention of electronic sensors has been applied to analyze the volatile organic compounds in breath to detect VAP using a machine learning technique. However, the process of building an algorithm is usually unclear and prevents physicians from applying the artificial intelligence technique in clinical practice. Clear processes of model building and assessing accuracy are warranted. The objective of this study was to develop a breath test for VAP with a standardized protocol for a machine learning technique. Methods: We conducted a case-control study. This study enrolled subjects in an intensive care unit of a hospital in southern Taiwan from February 2017 to June 2019. We recruited patients with VAP as the case group and ventilated patients without pneumonia as the control group. We collected exhaled breath and analyzed the electric resistance changes of 32 sensor arrays of an electronic nose. We split the data into a set for training algorithms and a set for testing. We applied eight machine learning algorithms to build prediction models, improving model performance and providing an estimated diagnostic accuracy. Results: A total of 33 cases and 26 controls were used in the final analysis. Using eight machine learning algorithms, the mean accuracy in the testing set was 0.81 ± 0.04, the sensitivity was 0.79 ± 0.08, the specificity was 0.83 ± 0.00, the positive predictive value was 0.85 ± 0.02, the negative predictive value was 0.77 ± 0.06, and the area under the receiver operator characteristic curves was 0.85 ± 0.04. The mean kappa value in the testing set was 0.62 ± 0.08, which suggested good agreement. Conclusions: There was good accuracy in detecting VAP by sensor array and machine learning techniques. Artificial intelligence has the potential to assist the physician in making a clinical diagnosis. Clear protocols for data processing and the modeling procedure needed to increase generalizability.

scholarly journals A Machine Learning Technique for Reducing Hospital Readmissions for Diabetic Diseases

2019 ◽  
Vol 9 (2) ◽  
pp. 4878-4884
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Operating Characteristic ◽  
Hospital Readmissions ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Diabetic Patients ◽  
Machine Learning Technique ◽  
Hospital Records ◽  
Learning Technique

The number of readmissions in diabetic diseases keeps increasing from time to time in patients from various hospitals. This brings a dreadful name to the hospital and is also considered as an act of irresponsibility of the doctors. So in order to reduce the readmissions of diabetic patients, we propose an approach which uses a machine learning technique to compare the hospital records of various patients. We have used various diabetic dataset features for our technique to predict the readmission probability rates of patients. We compared our proposed technique with existing Machine Learning algorithms like Random Forest, K-means clustering, Support Vector Machine(SVM) and found the best possible prediction with proposed approach using receiver operating characteristic( ROC) curve.

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