An optimal machine learning model for breast lesion classification based on random projection algorithm for feature optimization

2021 ◽  
Author(s):  
Morteza Heidari ◽  
Seyedehnafiseh Mirniaharikandehei ◽  
Abolfazl Zargari Khuzani ◽  
Gopichandh Danala ◽  
Hung Pham ◽  
...  
Keyword(s):  
Machine Learning ◽  
Breast Lesion ◽  
Random Projection ◽  
Learning Model ◽  
Projection Algorithm ◽  
Machine Learning Model ◽  
Feature Optimization ◽  
Optimal Machine ◽  
Lesion Classification

scholarly journals Applying a random projection algorithm to optimize machine learning model for breast lesion classification

2021 ◽  
pp. 1-1
Author(s):  
Morteza Heidari ◽  
Sivaramakrishnan Lakshmivarahan ◽  
Seyedehnafiseh Mirniaharikandehei ◽  
Gopichandh Danala ◽  
Sai Kiran R. Maryada ◽  
...  
Keyword(s):  
Machine Learning ◽  
Breast Lesion ◽  
Random Projection ◽  
Learning Model ◽  
Projection Algorithm ◽  
Machine Learning Model ◽  
Lesion Classification

scholarly journals Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements

2018 ◽  
Vol 20 (47) ◽  
pp. 29661-29668 ◽  
Author(s):  
Michael J. Willatt ◽  
Félix Musil ◽  
Michele Ceriotti
Keyword(s):  
Machine Learning ◽  
Periodic Table ◽  
Chemical Space ◽  
Learning Model ◽  
Data Driven ◽  
Machine Learning Model ◽  
Feature Optimization ◽  
Low Dimensional

By representing elements as points in a low-dimensional chemical space it is possible to improve the performance of a machine-learning model for a chemically-diverse dataset. The resulting coordinates are reminiscent of the main groups of the periodic table.

scholarly journals A New Ensenble Learning based Optimal Prediction Model for Cardiovascular Diseases

2021 ◽  
Vol 309 ◽  
pp. 01007
Author(s):  
B. Srınıvasa Rao
Keyword(s):  
Machine Learning ◽  
Cardiovascular Diseases ◽  
Present Model ◽  
Research Work ◽  
Learning Model ◽  
Ensemble Technique ◽  
Machine Learning Model ◽  
Learning Technique ◽  
Traditional Approaches ◽  
Optimal Machine

The present paperreports an optimal machine learning model for an effective prediction of cardiovascular diseases that uses the ensemble learning technique. The present research work gives an insight about the coherent way of combining Naive Bayes and Random Forest algorithm using ensemble technique. It also discusses how the present model is different from other traditional approaches. The present experimental results manifest that the present optimal machine learning model is more efficient than the other models.

scholarly journals Utility of Multi-Modal MRI for Differentiating of Parkinson's Disease and Progressive Supranuclear Palsy Using Machine Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Aron S. Talai ◽  
Jan Sedlacik ◽  
Kai Boelmans ◽  
Nils D. Forkert
Keyword(s):  
Machine Learning ◽  
Iron Metabolism ◽  
Learning Model ◽  
Learning Models ◽  
Brain Iron ◽  
Machine Learning Model ◽  
Brain Iron Metabolism ◽  
Microstructural Integrity ◽  
Machine Learning Models ◽  
Optimal Machine

Background: Patients with Parkinson's disease (PD) and progressive supranuclear palsy Richardson's syndrome (PSP-RS) often show overlapping clinical features, leading to misdiagnoses. The objective of this study was to investigate the feasibility and utility of using multi-modal MRI datasets for an automatic differentiation of PD patients, PSP-RS patients, and healthy control (HC) subjects.Material and Methods: T1-weighted, T2-weighted, and diffusion-tensor (DTI) MRI datasets from 45 PD patients, 20 PSP-RS patients, and 38 HC subjects were available for this study. Using an atlas-based approach, regional values of brain morphology (T1-weighted), brain iron metabolism (T2-weighted), and microstructural integrity (DTI) were measured and employed for feature selection and subsequent classification using combinations of various established machine learning methods.Results: The optimal machine learning model using regional morphology features only achieved a classification accuracy of 65% (67/103 correct classifications) differentiating PD patients, PSP-RS patients, and HC subjects. The optimal machine learning model using only quantitative T2 values performed slightly better and achieved an accuracy of 75.7% (78/103). The optimal classifier using DTI features alone performed considerably better with 95.1% accuracy (98/103). The optimal multi-modal classifier using all features also achieved an accuracy of 95.1% but required more features and achieved a slightly lower F1-score compared to the optimal model using DTI features alone.Conclusion: Machine learning models using multi-modal MRI perform significantly better than uni-modal machine learning models using morphological parameters based on T1-weighted MRI datasets alone or brain iron metabolism markers based on T2-weighted MRI datasets alone. However, machine learnig models using regional brain microstructural integrity metrics computed from DTI datasets perform similar to the optimal multi-modal machine learning model. Thus, given the results from this study cohort, it appears that morphology and brain iron metabolism markers may not provide additional value for classification compared to using DTI metrics alone.

Towards optimal machine learning model for terminal settling velocity

2021 ◽  
Author(s):  
Sayeed Rushd ◽  
Mohammad Tanvir Parvez ◽  
Majdi Adel Al-Faiad ◽  
Mohammed Monirul Islam
Keyword(s):  
Machine Learning ◽  
Settling Velocity ◽  
Learning Model ◽  
Machine Learning Model ◽  
Optimal Machine
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