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.

Wearable Devices Data for Activity Prediction Using Machine Learning Algorithms

2019 ◽  
Vol 4 (1) ◽  
pp. 32-46
Author(s):  
Lakshmi Prayaga ◽  
Krishna Devulapalli ◽  
Chandra Prayaga
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Wearable Devices ◽  
Machine Learning Algorithms ◽  
Embedded Sensors ◽  
Data Sets ◽  
Activity Prediction ◽  
Related Data ◽  
Recent Trends

Wearable devices are contributing heavily towards the proliferation of data and creating a rich minefield for data analytics. Recent trends in the design of wearable devices include several embedded sensors which also provide useful data for many applications. This research presents results obtained from studying human-activity related data, collected from wearable devices. The activities considered for this study were working at the computer, standing and walking, standing, walking, walking up and down the stairs, and talking while walking. The research entails the use of a portion of the data to train machine learning algorithms and build a model. The rest of the data is used as test data for predicting the activity of an individual. Details of data collection, processing, and presentation are also discussed. After studying the literature and the data sets, a Random Forest machine learning algorithm was determined to be best applicable algorithm for analyzing data from wearable devices. The software used in this research includes the R statistical package and the SensorLog app.

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

Increasing the Accuracy of Predictive Algorithms

2011 ◽  
pp. 1906-1910
Author(s):  
Sotiris Kotsiantis ◽  
Dimitris Kanellopoulos ◽  
Panayotis Pintelas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Bayesian Networks ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Data Sets ◽  
Combining Classifiers ◽  
Predictive Algorithms

In classification learning, the learning scheme is presented with a set of classified examples from which it is expected tone can learn a way of classifying unseen examples (see Table 1). Formally, the problem can be stated as follows: Given training data {(x1, y1)…(xn, yn)}, produce a classifier h: X- >Y that maps an object x ? X to its classification label y ? Y. A large number of classification techniques have been developed based on artificial intelligence (logic-based techniques, perception-based techniques) and statistics (Bayesian networks, instance-based techniques). No single learning algorithm can uniformly outperform other algorithms over all data sets. The concept of combining classifiers is proposed as a new direction for the improvement of the performance of individual machine learning algorithms. Numerous methods have been suggested for the creation of ensembles of classi- fiers (Dietterich, 2000). Although, or perhaps because, many methods of ensemble creation have been proposed, there is as yet no clear picture of which method is best.

scholarly journals Machine Learning Algorithms for Analysis of DNA Data Sets

2012 ◽  
pp. 47-58 ◽  
Author(s):  
John Yearwood ◽  
Adil Bagirov ◽  
Andrei V. Kelarev
Keyword(s):  
Machine Learning ◽  
Dna Sequences ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Local Alignment ◽  
Data Sets ◽  
Data Set ◽  
Applications Of Machine Learning ◽  
New Machine

The applications of machine learning algorithms to the analysis of data sets of DNA sequences are very important. The present chapter is devoted to the experimental investigation of applications of several machine learning algorithms for the analysis of a JLA data set consisting of DNA sequences derived from non-coding segments in the junction of the large single copy region and inverted repeat A of the chloroplast genome in Eucalyptus collected by Australian biologists. Data sets of this sort represent a new situation, where sophisticated alignment scores have to be used as a measure of similarity. The alignment scores do not satisfy properties of the Minkowski metric, and new machine learning approaches have to be investigated. The authors’ experiments show that machine learning algorithms based on local alignment scores achieve very good agreement with known biological classes for this data set. A new machine learning algorithm based on graph partitioning performed best for clustering of the JLA data set. Our novel k-committees algorithm produced most accurate results for classification. Two new examples of synthetic data sets demonstrate that the authors’ k-committees algorithm can outperform both the Nearest Neighbour and k-medoids algorithms simultaneously.

scholarly journals Comprehensive and empirical evaluation of machine learning algorithms for LC retention time prediction

2018 ◽  
Author(s):  
Robbin Bouwmeester ◽  
Lennart Martens ◽  
Sven Degroeve
Keyword(s):  
Machine Learning ◽  
Retention Time ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Mass Spectrometric ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Retention Time Prediction ◽  
Time Prediction ◽  
Different Types

AbstractLiquid chromatography is a core component of almost all mass spectrometric analyses of (bio)molecules. Because of the high-throughput nature of mass spectrometric analyses, the interpretation of these chromatographic data increasingly relies on informatics solutions that attempt to predict an analyte’s retention time. The key components of such predictive algorithms are the features these are supplies with, and the actual machine learning algorithm used to fit the model parameters.We here therefore evaluate the performance of seven machine learning algorithms on 36 distinct metabolomics data sets, using two distinct feature sets. Interestingly, the results show that no single learning algorithm performs optimally for all data sets, with different algorithm types achieving top performance for different types of analytes or different protocols. Our results can thus be used to find an optimal retention time prediction algorithm for specific analytes or protocols. Importantly, however, our results also show that blending different types of models together decreases the error on outliers, indicating that the combination of several approaches holds substantial promise for the development of more generic, high-performing algorithms.

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.

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.

scholarly journals Risk Monitoring and Quantitative Results of Various Attributes of Machine Learning Algorithms with a Time Series Data

2019 ◽  
Vol 8 (11) ◽  
pp. 4018-4022
Keyword(s):  
Machine Learning ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Machine Learning Algorithm ◽  
Risk Modelling ◽  
Risk Monitoring ◽  
Quantitative Results

The aim of this research is to do risk modelling after analysis of twitter posts based on certain sentiment analysis. In this research we analyze posts of several users or a particular user to check whether they can be cause of concern to the society or not. Every sentiment like happy, sad, anger and other emotions are going to provide scaling of severity in the conclusion of final table on which machine learning algorithm is applied. The data which is put under the machine learning algorithms are been monitored over a period of time and it is related to a particular topic in an area

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