scholarly journals A Novel System Design for Prognosis on Breast Cancer using Machine Learning Algorithms

2020 ◽  
Vol 9 (4) ◽  
pp. 1484-1490
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Early Stage ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
New Approach ◽  
Cancer Breast ◽  
Abnormal Cells ◽  
Deadly Disease

Cancer is the term used to describe a class of disease in which abnormal cells divide uncontrolledly and invade body tis sues. There are more than 100 unique types of cancer. Breast cancer is one of the women's deadly disease. The prediction is done at the earlier stage and the results are accurate, the number of death per year can be reduced. So ultimately a new approach is needed to predict the level of cancer at the early stage which shows accurate results on prediction level. Hence Machine learning algorithms are used to predict the level of accuracy. Henceforth this paper analyze the different machine learning algorithm to predict the best levels of cancer and comparative statement was made about accuracy and the results showing SVM is more accurate.

scholarly journals Breast Cancer Prediction using CNN and Machine Learning Algorithms with Comparative Analysis

2021 ◽  
Vol 9 (VI) ◽  
pp. 876-883
Author(s):  
Santhosh Voruganti
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Data Mining ◽  
Early Stage ◽  
Learning Algorithms ◽  
Malignant Tumour ◽  
Machine Learning Algorithms ◽  
Cancer Type ◽  
Cancer Disease ◽  
Cancer Breast

At present world, Breast cancer is a second main cause of cancer death in women after lung cancer. Breast cancer occurs when some breast cells begin to raise abnormally. It can arise in any portion of the Breast and it can be prevented if the treatment is started at the early stage of the Breast cancer. Breast cancer is a malignant tumour i.e. a collection of cancer cells arising from the cells of the breast Treatment of breast cancer relies on the cancer type and its stage. Mainly this paper focused on diagnosing the Breast cancer disease using various classification algorithm with the help of data mining tools. Data mining of the intelligent accumulated from previously disease detected patients opened up a new aspect of medical progression In this paper, the focus has been prediction of breast cancer using various machine learning algorithms and visualizing the performances of each algorithm. This paper makes use of a dataset that contains numerical values about the clump thickness, uniformity of the cell for prediction using Multi Layer Perceptron, K-NN, Random Forest, Logistic Regression. Moreover, this also uses a dataset of tissue images for the prediction using Convolution Neural Network. Later, the accuracies of each algorithm is calculated along with the precision, recall, f-score, ROC for each algorithm.

Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review (Preprint)

2021 ◽  
Author(s):  
Howard Maile ◽  
Ji-Peng Olivia Li ◽  
Daniel Gore ◽  
Marcello Leucci ◽  
Padraig Mulholland ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Progression ◽  
Learning Algorithm ◽  
Early Stage ◽  
Demographic Data ◽  
Learning Algorithms ◽  
Detailed Comparison ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Clinical Measures

BACKGROUND Keratoconus is a disorder characterized by progressive thinning and distortion of the cornea. If detected at an early stage corneal collagen cross linking can prevent disease progression and further visual loss. Whilst advanced forms are easily detected, reliably identifying subclinical disease can be problematic. A number of different machine learning algorithms have been used to improve the detection of subclinical keratoconus based on the analysis of single or multiple clinical measures such as corneal imaging, aberrometry, or biomechanical measurements. OBJECTIVE To survey and critically evaluate the literature on algorithmic detection of subclinical keratoconus and equivalent definitions. METHODS We performed a structured search of the following databases: Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (EMBASE), Web of Science and Cochrane from Jan 1, 2010 to Oct 31, 2020. We included all full text studies that have used algorithms for the detection of subclinical keratoconus. We excluded studies that did not perform validation. RESULTS We compared the parameters measured and the design of the machine learning algorithms reported in 26 papers that met the inclusion criteria. All salient information required for detailed comparison including diagnostic criteria, demographic data, sample size, acquisition system, validation details, parameter inputs, machine learning algorithm and key results are reported in this study. CONCLUSIONS Machine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Presently there is no consensus regarding the corneal parameters that should be included for assessment and the optimal design for the machine learning algorithm. We have identified avenues for further research to improve early detection and stratification of patients for early intervention to prevent disease progression. CLINICALTRIAL N/A

scholarly journals Performance Analysis of Machine Learning Algorithms for Cervical Cancer Detection

2020 ◽  
Vol 15 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Sanjay Kumar Singh ◽  
Anjali Goyal
Keyword(s):  
Machine Learning ◽  
Cervical Cancer ◽  
Pap Smear ◽  
Learning Algorithm ◽  
Early Stage ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Hybrid Techniques ◽  
Cpu Time

Cervical cancer is second most prevailing cancer in women all over the world and the Pap smear is one of the most popular techniques used to diagnosis cervical cancer at an early stage. Developing countries like India has to face the challenges in order to handle more cases day by day. In this article, various online and offline machine learning algorithms has been applied on benchmarked data sets to detect cervical cancer. This article also addresses the problem of segmentation with hybrid techniques and optimizes the number of features using extra tree classifiers. Accuracy, precision score, recall score, and F1 score are increasing in the proportion of data for training and attained up to 100% by some algorithms. Algorithm like logistic regression with L1 regularization has an accuracy of 100%, but it is too much costly in terms of CPU time in comparison to some of the algorithms which obtain 99% accuracy with less CPU time. The key finding in this article is the selection of the best machine learning algorithm with the highest accuracy. Cost effectiveness in terms of CPU time is also analysed.

scholarly journals Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Habib Dhahri ◽  
Eslam Al Maghayreh ◽  
Awais Mahmood ◽  
Wail Elkilani ◽  
Mohammed Faisal Nagi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Genetic Programming ◽  
Learning Algorithm ◽  
Empirical Studies ◽  
Learning Algorithms ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Algorithms ◽  
Programming Technique ◽  
Soft Computing Techniques

There have been several empirical studies addressing breast cancer using machine learning and soft computing techniques. Many claim that their algorithms are faster, easier, or more accurate than others are. This study is based on genetic programming and machine learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors. The aim of this study was to optimize the learning algorithm. In this context, we applied the genetic programming technique to select the best features and perfect parameter values of the machine learning classifiers. The performance of the proposed method was based on sensitivity, specificity, precision, accuracy, and the roc curves. The present study proves that genetic programming can automatically find the best model by combining feature preprocessing methods and classifier algorithms.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

A Robust Method to Predict Fluid Properties Based on Big Data and Machine Learning Algorithms

2021 ◽  
Author(s):  
Yingxian Liu ◽  
Cunliang Chen ◽  
Hanqing Zhao ◽  
Yu Wang ◽  
Xiaodong Han
Keyword(s):  
Machine Learning ◽  
Physical Properties ◽  
Learning Algorithm ◽  
Direct Method ◽  
Learning Algorithms ◽  
Small Error ◽  
Machine Learning Algorithms ◽  
Well Test ◽  
Empirical Formulas ◽  
Fluid Properties

Abstract Fluid properties are key factors for predicting single well productivity, well test interpretation and oilfield recovery prediction, which directly affect the success of ODP program design. The most accurate and direct method of acquisition is underground sampling. However, not every well has samples due to technical reasons such as excessive well deviation or high cost during the exploration stage. Therefore, analogies or empirical formulas have to be adopted to carry out research in many cases. But a large number of oilfield developments have shown that the errors caused by these methods are very large. Therefore, how to quickly and accurately obtain fluid physical properties is of great significance. In recent years, with the development and improvement of artificial intelligence or machine learning algorithms, their applications in the oilfield have become more and more extensive. This paper proposed a method for predicting crude oil physical properties based on machine learning algorithms. This method uses PVT data from nearly 100 wells in Bohai Oilfield. 75% of the data is used for training and learning to obtain the prediction model, and the remaining 25% is used for testing. Practice shows that the prediction results of the machine learning algorithm are very close to the actual data, with a very small error. Finally, this method was used to apply the preliminary plan design of the BZ29 oilfield which is a new oilfield. Especially for the unsampled sand bodies, the fluid physical properties prediction was carried out. It also compares the influence of the analogy method on the scheme, which provides potential and risk analysis for scheme design. This method will be applied in more oil fields in the Bohai Sea in the future and has important promotion value.

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