Guidelines to Select Machine Learning Scheme for Classification of Biomedical Datasets

2009 ◽  
pp. 128-139 ◽  
Author(s):  
Ajay Kumar Tanwani ◽  
Jamal Afridi ◽  
M. Zubair Shafiq ◽  
Muddassar Farooq
Keyword(s):  
Machine Learning ◽  
Learning Scheme

scholarly journals Hybrid Machine Learning Scheme for Classification of BECTS and TLE Patients Using EEG Brain Signals

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 218924-218935
Author(s):  
Wonsik Yang ◽  
Minsoo Joo ◽  
Yujaung Kim ◽  
Se Hee Kim ◽  
Jong-Moon Chung
Keyword(s):  
Machine Learning ◽  
Brain Signals ◽  
Learning Scheme ◽  
Hybrid Machine

Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme

2019 ◽  
Vol 61 (7) ◽  
pp. 757-765 ◽  
Author(s):  
Shai Shrot ◽  
Moshe Salhov ◽  
Nir Dvorski ◽  
Eli Konen ◽  
Amir Averbuch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumors ◽  
Perfusion Imaging ◽  
Diffusion Tensor ◽  
Learning Scheme

scholarly journals Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme

2009 ◽  
Vol 62 (6) ◽  
pp. 1609-1618 ◽  
Author(s):  
Evangelia I. Zacharaki ◽  
Sumei Wang ◽  
Sanjeev Chawla ◽  
Dong Soo Yoo ◽  
Ronald Wolf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Tumor Type ◽  
Learning Scheme

scholarly journals Random Forest Feature Selection, Fusion and Ensemble Strategy: Combining Multiple Morphological MRI Measures to Discriminate among healthy elderly, MCI, cMCI and Alzheimer's disease patients: from the Alzheimer’s disease neuroimaging initiative (ADNI) database

2017 ◽  
Author(s):  
S.I. Dimitriadis ◽  
D. Liparas ◽  
Magda N. Tsolaki
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Random Forest ◽  
Validation Dataset ◽  
Learning Scheme ◽  
Mri Features ◽  
Ensemble Strategy

AbstractBackgroundIn the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer’s disease (AD) covers a large percentage of neuroimaging research, with the main scope being its use in daily practice. However, there has been no study attempting to simultaneously discriminate among Healthy Controls (HC), early mild cognitive impairment (MCI), late MCI (cMCI) and stable AD, using features derived from a single modality, namely MRI.New MethodBased on preprocessed MRI images from the organizers of a neuroimaging challenge2, we attempted to quantify the prediction accuracy of multiple morphological MRI features to simultaneously discriminate among HC, MCI, cMCI and AD. We explored the efficacy of a novel scheme that includes multiple feature selections via Random Forest from subsets of the whole set of features (e.g. whole set, left/right hemisphere etc.), Random Forest classification using a fusion approach and ensemble classification via majority voting.From the ADNI database, 60 HC, 60 MCI, 60 cMCI and 60 AD were used as a training set with known labels. An extra dataset of 160 subjects (HC: 40, MCI: 40, cMCI: 40 and AD: 40) was used as an external blind validation dataset to evaluate the proposed machine learning scheme.ResultsIn the second blind dataset, we succeeded in a four-class classification of 61.9% by combining MRI-based features with a Random Forest-based Ensemble Strategy. We achieved the best classification accuracy of all teams that participated in this neuroimaging competition.Comparison with Existing Method(s)The results demonstrate the effectiveness of the proposed scheme to simultaneously discriminate among four groups using morphological MRI features for the very first time in the literature.ConclusionsHence, the proposed machine learning scheme can be used to define single and multi-modal biomarkers for AD.HIGHLIGHTS1st place in International Challenge for Automated Prediction of MCI from MRI DataMulti-class classification of normal control, MCI, converting MCI, and Alzheimer’s diseaseMorphometric measures from 3D T1 brain MRI images have been analysed (ADNI1 cohort).A Random Forest Feature Selection, Fusion and Ensemble Strategy was applied to classification and prediction of AD.Accuracy and robustness have been assessed in a blind dataset

A novel machine learning scheme for classification of medicinal herbs based on 2D-FTIR fingerprints

2022 ◽  
Vol 266 ◽  
pp. 120440
Author(s):  
Tiem Leong Yoon ◽  
Zhao Qin Yeap ◽  
Chu Shan Tan ◽  
Ying Chen ◽  
Jingying Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medicinal Herbs ◽  
Learning Scheme

Machine Learning Classification of Low-grade and High-grade Chondrosarcomas Based on MRI-based Texture Analysis

2019 ◽  
Author(s):  
S. Gitto ◽  
D. Albano ◽  
V. Chianca ◽  
R. Cuocolo ◽  
L. Ugga ◽  
...  
Keyword(s):  
Machine Learning ◽  
Texture Analysis ◽  
Low Grade ◽  
High Grade ◽  
Machine Learning Classification

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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