scholarly journals Efficient Discretization Approaches for Machine Learning Techniques to Improve Disease Classification on Gut Microbiome Composition Data

2020 ◽  
Vol 5 (3) ◽  
pp. 547-556
Author(s):  
Hai Thanh Nguyen ◽  
Nhi Yen Kim Phan ◽  
Huong Hoang Luong ◽  
Trung Phuoc Le ◽  
Nghi Cong Tran
Keyword(s):  
Machine Learning ◽  
Gut Microbiome ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Microbiome Composition ◽  
Composition Data ◽  
Learning Techniques

scholarly journals It takes guts to learn: machine learning techniques for disease detection from the gut microbiome

2021 ◽  
Author(s):  
Kristen D. Curry ◽  
Michael G. Nute ◽  
Todd J. Treangen
Keyword(s):  
Machine Learning ◽  
Gut Microbiome ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Neurological Deficits ◽  
Irritable Bowel Disease ◽  
Learning Techniques ◽  
Irritable Bowel ◽  
Microbiome Data ◽  
Future Work

Associations between the human gut microbiome and expression of host illness have been noted in a variety of conditions ranging from gastrointestinal dysfunctions to neurological deficits. Machine learning (ML) methods have generated promising results for disease prediction from gut metagenomic information for diseases including liver cirrhosis and irritable bowel disease, but have lacked efficacy when predicting other illnesses. Here, we review current ML methods designed for disease classification from microbiome data. We highlight the computational challenges these methods have effectively overcome and discuss the biological components that have been overlooked to offer perspectives on future work in this area.

Applications of Machine Learning Techniques in Disease Classification From Medical Images

2018 ◽  
pp. 318-337 ◽  
Author(s):  
Bikesh Kumar Singh ◽  
Satya Eswari Jujjavarapu
Keyword(s):  
Machine Learning ◽  
Medical Images ◽  
Radial Basis Function Network ◽  
Objective Evaluation ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Future Challenges ◽  
Artificial Neural Network Ann ◽  
Applications Of Machine Learning

Machine learning techniques such as artificial neural network (ANN), support vector machine (SVM), radial basis function network (RBFN), random forest (RF), naive Bayes classifier, etc. have gained much attention in recent years due to their widespread applications in diverse fields. This chapter is focused on providing a comprehensive insight of various techniques employed for key areas of medical image processing and analysis. Different applications covered in this chapter include feature extraction, feature selection, and cancer classification in medical images. The authors present current practices and evaluation measures used for objective evaluation of different machine learning methods in context to above-mentioned applications. Various factors associated with acceptance/rejection of such automated systems by medical research community are discussed. The authors also discuss how the interaction between automated analysis systems and medical professionals can be improved for its acceptance in clinical practice. They conclude the chapter by presenting research gaps and future challenges.

Skin Disease Classification Using Machine Learning Techniques

2021 ◽  
pp. 597-608
Author(s):  
Mohammad Ashraful Haque Abir ◽  
Golam Kibria Anik ◽  
Shazid Hasan Riam ◽  
Mohammed Ariful Karim ◽  
Azizul Hakim Tareq ◽  
...  
Keyword(s):  
Machine Learning ◽  
Skin Disease ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Learning Techniques

Automated Corn Seed Fusarium Disease Classification System Using Hybrid Feature Space and Conventional Machine Learning Techniques

2021 ◽  
Vol 58 (2) ◽  
pp. 1-10
Author(s):  
Samreen Naeem ◽  
Aqib Ali ◽  
Jamal Abdul Nasir ◽  
Arooj Fatima ◽  
Farrukh Jamal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Space ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Corn Seed ◽  
Area Of Interest ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Correlation Based Feature Selection ◽  
Conventional Machine

The purpose of this learning is to detect the Corn Seed Fusarium Disease using Hybrid Feature Space and Conventional machine learning (ML) approaches. A novel machine learning approach is employed for the classification of a total of six types of corn seed are collected which contain Infected Fusarium (moniliforme, graminearum, gibberella, verticillioides, kernel) as well as healthy corn seed, based on a multi-feature dataset, which is the grouping of geometric, texture and histogram features extracted from digital images. For each corn seed image, a total of twenty-five multi-features have been developed on every area of interest (AOI), sizes (50 × 50), (100 × 100), (150 × 150), and (200 × 200). A total of seven optimized features were selected by using a machine learning-based algorithm named “Correlation-based Feature Selection”. For experimentation, “Random forest”, “BayesNet” and “LogitBoost” have been employed using an optimized multi-feature user-supplied dataset divided with 70% training and 30 % testing. A comparative analysis of three ML classifiers RF, BN, and LB have been used and a considerably very high classification ratio of 96.67 %, 97.22 %, and 97.78 % have been achieved respectively when the AOI size (200×200) have been deployed to the classifiers.

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