Machine learning algorithms using binary classification and multi model ensemble techniques for skin diseases prediction

Abstract Binary classification is a widely employed problem to facilitate the decisions on various biomedical big data questions, such as clinical drug trials between treated participants and controls, and genome-wide association studies (GWASs) between participants with or without a phenotype. A machine learning model is trained for this purpose by optimizing the power of discriminating samples from two groups. However, most of the classification algorithms tend to generate one locally optimal solution according to the input dataset and the mathematical presumptions of the dataset. Here we demonstrated from the aspects of both disease classification and feature selection that multiple different solutions may have similar classification performances. So the existing machine learning algorithms may have ignored a horde of fishes by catching only a good one. Since most of the existing machine learning algorithms generate a solution by optimizing a mathematical goal, it may be essential for understanding the biological mechanisms for the investigated classification question, by considering both the generated solution and the ignored ones.

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Skin Characterizations by Using Contact Capacitive Imaging and High-Resolution Ultrasound Imaging with Machine Learning Algorithms

Applied Sciences ◽

10.3390/app11188714 ◽

2021 ◽

Vol 11 (18) ◽

pp. 8714

Author(s):

Elena Chirikhina ◽

Andrey Chirikhin ◽

Sabina Dewsbury-Ennis ◽

Francesco Bianconi ◽

Perry Xiao

Keyword(s):

Machine Learning ◽

High Resolution ◽

Stratum Corneum ◽

Image Classification ◽

Water Content ◽

Ultrasound Imaging ◽

Skin Diseases ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Body Parts

We present our latest research on skin characterizations by using Contact Capacitive Imaging and High-Resolution Ultrasound Imaging with Machine Learning algorithms. Contact Capacitive Imaging is a novel imaging technology based on the dielectric constant measurement principle, with which we have studied the skin water content of different skin sites and performed image classification by using pre-trained Deep Learning Neural Networks through Transfer Learning. The results show lips and nose have the lowest water content, whilst cheek, eye corner and under-eye have the highest water content. The classification yields up to 83.8% accuracy. High-Resolution Ultrasound Imaging is a state-of-the-art ultrasound technology, and can produce high-resolution images of the skin and superficial soft tissue to a vertical resolution of about 40 microns, with which we have studied the thickness of different skin layers, such as stratum corneum, epidermis and dermis, around different locations on the face and around different body parts. The results show the chin has the highest stratum corneum thickness, and the arm has the lowest stratum corneum thickness. We have also developed two feature-based image classification methods which yield promising results. The outcomes of this study could provide valuable guidelines for cosmetic/medical research, and methods developed in this study can also be extended for studying damaged skin or skin diseases. The combination of Contact Capacitive Imaging and High-Resolution Ultrasound Imaging could be a powerful tool for skin studies.

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Landslide detection by machine learning on high-resolution DEMs

10.5194/egusphere-egu21-13635 ◽

2021 ◽

Author(s):

Mihai Niculita

Keyword(s):

Machine Learning ◽

High Resolution ◽

Binary Classification ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Single Event ◽

High Resolution Data ◽

Susceptibility Modeling ◽

Study Case ◽

High Level

<p>Machine learning algorithms are increasingly used in geosciences for the detection of susceptibility modeling of certain landforms or processes. The increased availability of high-resolution data and the increase of available machine learning algorithms opens up the possibility of creating datasets for the training of models for automatic detection of specific landforms. In this study, we tested the usage of LiDAR DEMs for creating a dataset of labeled images representing shallow single event landslides in order to use them for the detection of other events. The R stat implementation of the keras high-level neural networks API was used to build and test the proposed approach. A 5m LiDAR DEM was cut in 25 by 25 pixels tiles, and the tiles that overlayed shallow single event landslides were labeled accordingly, while the tiles that did not contain landslides were randomly selected to be labeled as non-landslides. The binary classification approach was tested with 255 grey levels elevation images and 255 grey levels shading images, the shading approach giving better results. The presented study case shows the possibility of using machine learning in the landslide detection on high-resolution DEMs.</p>

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Machine Learning Methods for Fear Classification Based on Physiological Features

Sensors ◽

10.3390/s21134519 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4519

Author(s):

Livia Petrescu ◽

Cătălin Petrescu ◽

Ana Oprea ◽

Oana Mitruț ◽

Gabriela Moise ◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Dimensionality Reduction ◽

Data Augmentation ◽

Binary Classification ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Physiological Data ◽

Gradient Boosting ◽

Support Vector

This paper focuses on the binary classification of the emotion of fear, based on the physiological data and subjective responses stored in the DEAP dataset. We performed a mapping between the discrete and dimensional emotional information considering the participants’ ratings and extracted a substantial set of 40 types of features from the physiological data, which represented the input to various machine learning algorithms—Decision Trees, k-Nearest Neighbors, Support Vector Machine and artificial networks—accompanied by dimensionality reduction, feature selection and the tuning of the most relevant hyperparameters, boosting classification accuracy. The methodology we approached included tackling different situations, such as resolving the problem of having an imbalanced dataset through data augmentation, reducing overfitting, computing various metrics in order to obtain the most reliable classification scores and applying the Local Interpretable Model-Agnostic Explanations method for interpretation and for explaining predictions in a human-understandable manner. The results show that fear can be predicted very well (accuracies ranging from 91.7% using Gradient Boosting Trees to 93.5% using dimensionality reduction and Support Vector Machine) by extracting the most relevant features from the physiological data and by searching for the best parameters which maximize the machine learning algorithms’ classification scores.

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