scholarly journals The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Omer F. Akmese ◽  
Gul Dogan ◽  
Hakan Kor ◽  
Hasan Erbay ◽  
Emre Demir
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Acute Appendicitis ◽  
Learning Algorithms ◽  
Estimation Method ◽  
Machine Learning Algorithms ◽  
Accurate Estimation ◽  
Learning Approaches ◽  
Predictive Data Mining ◽  
Clinical Records

Acute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery. The study aims to develop an easy, fast, and accurate estimation method for early acute appendicitis diagnosis using machine learning algorithms. Retrospective clinical records were analyzed with predictive data mining models. The predictive success of the models obtained by various machine learning algorithms was compared. A total of 595 clinical records were used in the study, including 348 males (58.49%) and 247 females (41.51%). It was found that the gradient boosted trees algorithm achieves the best success with an accurate prediction success of 95.31%. In this study, an estimation method based on machine learning was developed to identify individuals with acute appendicitis. It is thought that this method will benefit patients with signs of appendicitis, especially in emergency departments in hospitals.

scholarly journals Searching for improvements in predicting human eye colour from DNA

2021 ◽  
Author(s):  
Magdalena Kukla-Bartoszek ◽  
Paweł Teisseyre ◽  
Ewelina Pośpiech ◽  
Joanna Karłowska-Pik ◽  
Piotr Zieliński ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sequencing Analysis ◽  
Learning Approaches ◽  
Human Eye ◽  
Software Analysis ◽  
Whole Exome ◽  
Eye Colour

AbstractIncreasing understanding of human genome variability allows for better use of the predictive potential of DNA. An obvious direct application is the prediction of the physical phenotypes. Significant success has been achieved, especially in predicting pigmentation characteristics, but the inference of some phenotypes is still challenging. In search of further improvements in predicting human eye colour, we conducted whole-exome (enriched in regulome) sequencing of 150 Polish samples to discover new markers. For this, we adopted quantitative characterization of eye colour phenotypes using high-resolution photographic images of the iris in combination with DIAT software analysis. An independent set of 849 samples was used for subsequent predictive modelling. Newly identified candidates and 114 additional literature-based selected SNPs, previously associated with pigmentation, and advanced machine learning algorithms were used. Whole-exome sequencing analysis found 27 previously unreported candidate SNP markers for eye colour. The highest overall prediction accuracies were achieved with LASSO-regularized and BIC-based selected regression models. A new candidate variant, rs2253104, located in the ARFIP2 gene and identified with the HyperLasso method, revealed predictive potential and was included in the best-performing regression models. Advanced machine learning approaches showed a significant increase in sensitivity of intermediate eye colour prediction (up to 39%) compared to 0% obtained for the original IrisPlex model. We identified a new potential predictor of eye colour and evaluated several widely used advanced machine learning algorithms in predictive analysis of this trait. Our results provide useful hints for developing future predictive models for eye colour in forensic and anthropological studies.

scholarly journals Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences

2015 ◽  
Vol 32 (6) ◽  
pp. 821-827 ◽  
Author(s):  
Enrique Audain ◽  
Yassel Ramos ◽  
Henning Hermjakob ◽  
Darren R. Flower ◽  
Yasset Perez-Riverol
Keyword(s):  
Machine Learning ◽  
Isoelectric Point ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Basis Set ◽  
Superior Performance ◽  
Supplementary Information ◽  
Training Dataset ◽  
Accurate Estimation ◽  
Prediction Methods

Abstract Motivation: In any macromolecular polyprotic system—for example protein, DNA or RNA—the isoelectric point—commonly referred to as the pI—can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge—and thus the electrophoretic mobility—of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods. Results: Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction. Contact: [email protected] Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR. Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals Predicting Student Failure in University Examination using Machine Learning Algorithms

2020 ◽  
Vol 9 (5) ◽  
pp. 956-959
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Performance Management ◽  
Student Performance ◽  
Learning Algorithms ◽  
Educational Data Mining ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Social Characteristics ◽  
Student Failure

Student Performance Management is one of the key pillars of the higher education institutions since it directly impacts the student’s career prospects and college rankings. This paper follows the path of learning analytics and educational data mining by applying machine learning techniques in student data for identifying students who are at the more likely to fail in the university examinations and thus providing needed interventions for improved student performance. The Paper uses data mining approach with 10 fold cross validation to classify students based on predictors which are demographic and social characteristics of the students. This paper compares five popular machine learning algorithms Rep Tree, Jrip, Random Forest, Random Tree, Naive Bayes algorithms based on overall classifier accuracy as well as other class specific indicators i.e. precision, recall, f-measure. Results proved that Rep tree algorithm outperformed other machine learning algorithms in classifying students who are at more likely to fail in the examinations.

scholarly journals A Review on Various Algorithms used in Machine Learning

2019 ◽  
pp. 915-920
Author(s):  
Divya Chaudhary ◽  
Er. Richa Vasuja
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
New Technologies ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning System ◽  
Training Set ◽  
Learning Data ◽  
Do So

In today's scenario all of data is being generated by everyone of us . so it becomes vital for us to handle this data. To do so new technologies are being developed such as machine learning, data mining etc. This paper gives the study related to machine learning(ML).Precise approximations are repetitively being produced by Machine Learning algorithms. Machine learning system effectively “learns” how to guess from training set of completed jobs. The main purpose of the review is to give a jagged estimate or overview about the mostly used algorithms in machine learning.

scholarly journals Heart Disease Prediction Using Machine Learning

2021 ◽  
pp. 267-276
Author(s):  
Baban. U. Rindhe ◽  
Nikita Ahire ◽  
Rupali Patil ◽  
Shweta Gagare ◽  
Manisha Darade
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Whole Body ◽  
Support Vector ◽  
Learning Techniques

Heart-related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need fora reliable, accurate, and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart-related diseases. Heart is the next major organ comparing to the brain which has more priority in the Human body. It pumps the blood and supplies it to all organs of the whole body. Prediction of occurrences of heart diseases in the medical field is significant work. Data analytics is useful for prediction from more information and it helps the medical center to predict various diseases. A huge amount of patient-related data is maintained on monthly basis. The stored data can be useful for the source of predicting the occurrence of future diseases. Some of the data mining and machine learning techniques are used to predict heart diseases, such as Artificial Neural Network (ANN), Random Forest,and Support Vector Machine (SVM).Prediction and diagnosingof heart disease become a challenging factor faced by doctors and hospitals both in India and abroad. To reduce the large scale of deaths from heart diseases, a quick and efficient detection technique is to be discovered. Data mining techniques and machine learning algorithms play a very important role in this area. The researchers accelerating their research works to develop software with thehelp of machine learning algorithms which can help doctors to decide both prediction and diagnosing of heart disease. The main objective of this research project is to predict the heart disease of a patient using machine learning algorithms.

scholarly journals Heart disease prediction using Advanced Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 2160-2163
Author(s):  
Minal Shahakar
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Web Application ◽  
Intelligent System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Disease Prediction ◽  
End User ◽  
Data Mining Techniques

It might have happened so many times that you or someone yours need doctors help immediately, but they are not available due to some reason. The Heart Disease Prediction application is an end user support to the online. Here, we propose a web application that allows users to get instant guidance on their heart disease through an intelligent system online. The application is fed with various details and the heart disease associated with those details. The applications allows user to share their heart related issues. It then processes user specific details to check for various illnesses that could be associated with it. Here we use some intelligent data mining techniques to the most accurate that could be associated with patient‟s details. Based on result, system automatically shows the result specific doctors for further treatment and the system allows user to view doctor‟s details.

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