Classification methods for the analysis of LH-PCR data associated with inflammatory bowel disease patients

Wisittipanit, Nuttachat; Rangwala, Huzefa; Sikaroodi, Masoumeh; Keshavarzian, Ali; Mutlu, Ece; Gillevet, Patrick M.

doi:10.1504/ijbra.2015.068087

Cited by 11 publications

(7 citation statements)

References 33 publications

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“…Supervised machine learning methods are useful for finding patterns in highly complex data sets like human microbiota surveys 44. Moreover, we introduced informative feature selection as an additional step for classification to find a combination of feature subsets that would lead to better classifiers 45.…”

Section: Discussionmentioning

confidence: 99%

Gut microbiome identifies risk for colorectal polyps

Dadkhah

Sikaroodi

Korman

et al. 2019

BMJ Open Gastroenterol

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ObjectiveTo characterise the gut microbiome in subjects with and without polyps and evaluate the potential of the microbiome as a non-invasive biomarker to screen for risk of colorectal cancer (CRC).DesignPresurgery rectal swab, home collected stool, and sigmoid biopsy samples were obtained from 231 subjects undergoing screening or surveillance colonoscopy. 16S rRNA analysis was performed on 552 samples (231 rectal swab, 183 stool, 138 biopsy) and operational taxonomic units (OTU) were identified using UPARSE. Non-parametric statistical methods were used to identify OTUs that were significantly different between subjects with and without polyps. These informative OTUs were then used to build classifiers to predict the presence of polyps using advanced machine learning models.ResultsWe obtained clinical data on 218 subjects (87 females, 131 males) of which 193 were White, 21 African-American, and 4 Asian-American. Colonoscopy detected polyps in 56% of subjects. Modelling of the non-invasive home stool samples resulted in a classification accuracy >75% for Naïve Bayes and Neural Network models using informative OTUs. A naïve holdout analysis performed on home stool samples resulted in an average false negative rate of 11.5% for the Naïve Bayes and Neural Network models, which was reduced to 5% when the two models were combined.ConclusionGut microbiome analysis combined with advanced machine learning represents a promising approach to screen patients for the presence of polyps, with the potential to optimise the use of colonoscopy, reduce morbidity and mortality associated with CRC, and reduce associated healthcare costs.

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Section: Discussionmentioning

confidence: 99%

Gut microbiome identifies risk for colorectal polyps

Dadkhah

Sikaroodi

Korman

et al. 2019

BMJ Open Gastroenterol

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“…Zuo et al [117] proposed an adaptive fuzzy KNN approach for an efficient Parkinson's disease diagnosis. Wisittipanit et al [118] analysed length heterogeneity polymerase chain reaction (LH-PCR) associated with inflammatory bowel disease studying the relationships between some microbial communities within the human gut and their roles in disease. Papakostas et al [119] diagnosed Alzheimer's disease based on magnetic resonance imaging data features and applying a lattice computing scheme.…”

Section: Applicationsmentioning

confidence: 99%

“…Metabolic diseases [74,79] Clustering K-means Clustering [87] Clustering DBSCAN [171] Regression Random Forest [100] Classification SVM [106,109] Classification ID3 [115,116,118,120,122] Classification KNN [135] Classification Naïve Bayes [137,143] Classification Bayesian Networks [145] Regression Linear regression Cancer [75,81] Clustering K-means Clustering [84,86] Clustering DBSCAN [24] Clustering SNF [25] Clustering PINS [26] Clustering CIMLR [95,172] Classification SVM [108] Classification ID3 [130] Classification Naïve Bayes [136] Classification Bayesian Networks [148,173] Regression Linear regression [146,174] Regression Logistic regression [157] Classification Neural Networks + KNN [156] Classification Neural Networks + SVM [160] Classification Neural Networks + ID3 [161] Classification KNN [175] Classification DT [176] Classification DL…”

Section: Author Goal Algorithmmentioning

confidence: 99%

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

et al. 2020

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Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.

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“…studies of microorganism roles to human health and diseases. As such, there are studies showing that alteration of the microbial communities could also cause or is a result of some diseases such as obesity and inflammatory bowel diseases [5]. For most of microbe studies, use of specific preserved genes i.e.…”

Section: Introductionmentioning

confidence: 99%

“…The model selection frameworks with three classification algorithms: Naïve Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machines (SVM) along with attribute selection technique were used to classify between the microbe community samples of Crohn's disease patients and those of Ulcerative Colitis patients at different human GI tract sites. The differences between microbial samples of IBD disease patients and those of healthy ones were already investigated and the results show that there might exist differentially abundant OTU in various GI locations [5].…”

Section: Introductionmentioning

confidence: 99%

Comparative Computational Analysis of Microbe Data between Inflammatory Bowel Diseases

2015

International Institute of Engineers (IIE) June 18-19, 2015 Pattaya (Thailand)

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The intestinal tracts in the human gut is the host of trillions and trillions of microorganisms in both commensal and pathogenic forms. In fact, the microbial communities in the human gastrointestinal (GI) tract is one of the most crowded population on earth. The relationships of these microorganisms with the human GI tract is responsible for human health and several diseases. It is essential to comprehend their roles to human ailments related to the GI tract. The computational pipeline combined with attribute selection and model selection technique was employed to investigate the differences in microbial community samples between two prevalent Inflammatory Bowel Diseases (IBD): Crohn's disease and Ulcerative Colitis using Length Heterogeneity Polymerase Chain Reaction (LH-PCR) data extracted from the various human GI tract locations. The purpose was to obtain understandings into the microbes correlated with IBD diseases.

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Classification methods for the analysis of LH-PCR data associated with inflammatory bowel disease patients

Cited by 11 publications

References 33 publications

Gut microbiome identifies risk for colorectal polyps

Gut microbiome identifies risk for colorectal polyps

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

Comparative Computational Analysis of Microbe Data between Inflammatory Bowel Diseases

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