Time-Lagged Prediction of Food Craving with Elastic Net Regression and BISCWIT: Prediction Performance of Qualitative Distinct Predictor Types

Mapping Intimacies ◽

10.31234/osf.io/5hj3x ◽

2020 ◽

Author(s):

Tim Kaiser ◽

Björn Butter

Keyword(s):

Binary Classification ◽

Elastic Net ◽

Sensor Data ◽

Food Craving ◽

Food Cravings ◽

Passive Sensing ◽

Psychometric Data ◽

Time Lagged ◽

Ecological Momentary

Food craving (FC) peaks are highly context-dependent and variable. Accurate prediction of FC might help preventing disadvantageous eating behavior. Here, we examine whether data from two weeks of Ecological Momentary Assessment questionnaires on stress and emotions alongside smartphone sensor data would be able to predict food cravings approximately 2.5 hours into the future in N = 46 individuals. We compared two prediction approaches (logistic BISCWIT and Elastic Net Regression (ENR). While overall prediction accuracy was low, passive sensing data alone was superior to predictions based on psychometric data and BISCWIT was superior to ENR for binary classification of FC peaks.

Download Full-text

Time-Lagged Prediction of Food Craving With Qualitative Distinct Predictor Types: An Application of BISCWIT

Frontiers in Digital Health ◽

10.3389/fdgth.2021.694233 ◽

2021 ◽

Vol 3 ◽

Author(s):

Tim Kaiser ◽

Björn Butter ◽

Samuel Arzt ◽

Björn Pannicke ◽

Julia Reichenberger ◽

...

Keyword(s):

Sensor Data ◽

Food Craving ◽

Passive Sensing ◽

Temporal Features ◽

Psychometric Data ◽

Time Lagged ◽

Feature Dimension ◽

Ecological Momentary ◽

Prediction Approach ◽

Momentary Assessment

Food craving (FC) peaks are highly context-dependent and variable. Accurate prediction of FC might help preventing disadvantageous eating behavior. Here, we examine whether data from 2 weeks of ecological momentary assessment (EMA) questionnaires on stress and emotions (active EMA, aEMA) alongside temporal features and smartphone sensor data (passive EMA, pEMA) are able to predict FCs ~2.5 h into the future in N = 46 individuals. A logistic prediction approach with feature dimension reduction via Best Item Scale that is Cross-Validated, Weighted, Informative and Transparent (BISCWIT) was performed. While overall prediction accuracy was acceptable, passive sensing data alone was equally predictive to psychometric data. The frequency of which single predictors were considered for a model was rather balanced, indicating that aEMA and pEMA models were fully idiosyncratic.

Download Full-text

Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones

Sensors ◽

10.3390/s20123572 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3572 ◽

Cited By ~ 1

Author(s):

Nicholas C. Jacobson ◽

Yeon Joo Chung

Keyword(s):

Depressed Mood ◽

Wearable Sensors ◽

Smartphone Application ◽

Sensor Data ◽

Just In Time ◽

Modeling Framework ◽

Daily Lives ◽

Passive Sensing ◽

Out Of Sample ◽

Ecological Momentary

Prior research has recently shown that passively collected sensor data collected within the contexts of persons daily lives via smartphones and wearable sensors can distinguish those with major depressive disorder (MDD) from controls, predict MDD severity, and predict changes in MDD severity across days and weeks. Nevertheless, very little research has examined predicting depressed mood within a day, which is essential given the large amount of variation occurring within days. The current study utilized passively collected sensor data collected from a smartphone application to future depressed mood from hour-to-hour in an ecological momentary assessment study in a sample reporting clinical levels of depression (N = 31). Using a combination of nomothetic and idiographically-weighted machine learning models, the results suggest that depressed mood can be accurately predicted from hour to hour with an average correlation between out of sample predicted depressed mood levels and observed depressed mood of 0.587, CI [0.552, 0.621]. This suggests that passively collected smartphone data can accurately predict future depressed mood among a sample reporting clinical levels of depression. If replicated in other samples, this modeling framework may allow just-in-time adaptive interventions to treat depression as it changes in the context of daily life.

Download Full-text

Method of determination of the text direction on the image with the use of convolutional neural network

Informatization and communication ◽

10.34219/2078-8320-2020-11-2-96-99 ◽

2020 ◽

pp. 96-99

Author(s):

P.L. Nikolaev

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Deep Neural Network ◽

Binary Classification ◽

Synthetic Data ◽

Real Data ◽

Method Of Determination ◽

Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

Download Full-text

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

Recent Patents on Engineering ◽

10.2174/1872212114999201109205421 ◽

2020 ◽

Vol 14 ◽

Author(s):

Lahari Tipirneni ◽

Rizwan Patan

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Convolutional Neural Network ◽

Binary Classification ◽

Disease Diagnosis ◽

Feature Descriptors ◽

Histopathological Images ◽

Viable Approach ◽

Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

Download Full-text

Improving Land Cover Classification Using Genetic Programming for Feature Construction

Remote Sensing ◽

10.3390/rs13091623 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1623

Author(s):

João E. Batista ◽

Ana I. R. Cabral ◽

Maria J. P. Vasconcelos ◽

Leonardo Vanneschi ◽

Sara Silva

Keyword(s):

Land Cover ◽

Genetic Programming ◽

Satellite Images ◽

State Of The Art ◽

Binary Classification ◽

Feature Construction ◽

Classification Problems ◽

Construction Methods ◽

Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

Download Full-text

Monotonic Functions Method and Its Application to Staging of Patients with Prostate Cancer According to Pretreatment Data

Applied Sciences ◽

10.3390/app11093836 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3836

Author(s):

Valeri Gitis ◽

Alexander Derendyaev ◽

Konstantin Petrov ◽

Eugene Yurkov ◽

Sergey Pirogov ◽

...

Keyword(s):

Prostate Cancer ◽

Learning Algorithm ◽

Binary Classification ◽

Preoperative Staging ◽

Threshold Value ◽

Logical Function ◽

Adequate Treatment ◽

Monotonic Functions ◽

Selection Of

Prostate cancer is the second most frequent malignancy (after lung cancer). Preoperative staging of PCa is the basis for the selection of adequate treatment tactics. In particular, an urgent problem is the classification of indolent and aggressive forms of PCa in patients with the initial stages of the tumor process. To solve this problem, we propose to use a new binary classification machine-learning method. The proposed method of monotonic functions uses a model in which the disease’s form is determined by the severity of the patient’s condition. It is assumed that the patient’s condition is the easier, the less the deviation of the indicators from the normal values inherent in healthy people. This assumption means that the severity (form) of the disease can be represented by monotonic functions from the values of the deviation of the patient’s indicators beyond the normal range. The method is used to solve the problem of classifying patients with indolent and aggressive forms of prostate cancer according to pretreatment data. The learning algorithm is nonparametric. At the same time, it allows an explanation of the classification results in the form of a logical function. To do this, you should indicate to the algorithm either the threshold value of the probability of successful classification of patients with an indolent form of PCa, or the threshold value of the probability of misclassification of patients with an aggressive form of PCa disease. The examples of logical rules given in the article show that they are quite simple and can be easily interpreted in terms of preoperative indicators of the form of the disease.

Download Full-text