Panic attack prediction using wearable devices and machine learning: development and cohort study (Preprint)

2021 ◽  
Author(s):  
Chan-Hen Tsai ◽  
Pei-Chen Chen ◽  
Chia-Tung Wu ◽  
Ying-Ying Kuo ◽  
Tsung-Ting Hsieh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Air Quality ◽  
Prediction Model ◽  
Panic Attack ◽  
Emergency Department Visits ◽  
Machine Learning Algorithms ◽  
Activity Level ◽  
Environmental Data ◽  
Panic Attacks

BACKGROUND A panic attack (PA) is an intense form of anxiety accompanied by multiple somatic presentations, leading to frequent emergency department visits and repetitive medical examinations before a formal diagnosis. Designing a PA prediction model could help prevent iatrogenic harm to patients and facilitate more personalized treatment. OBJECTIVE This study aimed to provide a seven-day PA prediction model and determine the relationship between physiological factors, anxiety and depressive factors, and air quality index. METHODS We enrolled 59 participants with PD (DMS-5 and MINI interview). Participants used smartwatches (Garmin vivosmart 4) and mobile applications to collect their sleep, heart rate, activity level, anxiety, and depression scores (BDI, BAI, STAI-S, STAI-T, and PDSS-SR) in their real life for a duration of one year. We also included air quality indexes from open data. To analyze these data, our team used six machine learning methods: random forests, decision trees, LDA, AdaBoost, XgBoost, and regularized greedy forests. RESULTS For seven-day PA prediction, the random forest produced the best prediction rate. The model achieved an accuracy of 97.5% on the training set. Overall, the accuracy of the testing set was 67.4–81.3% for different machine learning algorithms. The most critical variables in the model were questionnaire and physiological features such as BAI, BDI, STAI, MINI, average heart rate, resting heart rate, and deep sleep duration. CONCLUSIONS It is possible to predict panic attacks using a combination of data from questionnaires, and physiological and environmental data. Prediction accuracy was 97.5% on the training data and 81.3% on the testing data.

scholarly journals Comparison of Climate Reanalysis and Remote-Sensing Data for Predicting Olive Phenology through Machine-Learning Methods

2021 ◽  
Vol 13 (6) ◽  
pp. 1224
Author(s):  
Izar Azpiroz ◽  
Noelia Oses ◽  
Marco Quartulli ◽  
Igor G. Olaizola ◽  
Diego Guidotti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Prediction Model ◽  
Olive Tree ◽  
Temperature Data ◽  
Machine Learning Algorithms ◽  
Environmental Data ◽  
Climate Data ◽  
Tree Phenology ◽  
The Impact

Machine-learning algorithms used for modelling olive-tree phenology generally and largely rely on temperature data. In this study, we developed a prediction model on the basis of climate data and geophysical information. Remote measurements of weather conditions, terrain slope, and surface spectral reflectance were considered for this purpose. The accuracy of the temperature data worsened when replacing weather-station measurements with remote-sensing records, though the addition of more complete environmental data resulted in an efficient prediction model of olive-tree phenology. Filtering and embedded feature-selection techniques were employed to analyze the impact of variables on olive-tree phenology prediction, facilitating the inclusion of measurable information in decision support frameworks for the sustainable management of olive-tree systems.

PanicMechanic: A Digital Therapeutic Delivering Biofeedback for Panic Attacks (Preprint)

2021 ◽  
Author(s):  
Ellen McGinnis ◽  
Aisling O'Leary ◽  
Reed Gurchiek ◽  
William Copeland ◽  
Ryan McGinnis
Keyword(s):  
Mental Health ◽  
Heart Rate ◽  
Pilot Study ◽  
Alcohol Consumption ◽  
Future Development ◽  
Real World ◽  
Panic Attack ◽  
Eating Habits ◽  
Controlled Trial ◽  
Panic Attacks

UNSTRUCTURED Panic attacks are an impairing mental health problem that affects more than 11% of adults every year. Panic attacks are episodic, and it is difficult to predict when or where they may occur, thus they are challenging to study and treat. To this end, we present PanicMechanic, a novel mobile health (mHealth) application that captures heartrate-based data and delivers biofeedback during panic attacks. We leverage this tool to capture profiles of real-world panic attacks in a largest sample to date and present results from a pilot study to assess the feasibility and usefulness of PanicMechanic as a panic attack intervention. Results demonstrate that heart rate fluctuates by about 15 beats per minute during a panic attack and takes about 30 seconds to return to baseline from peak, cycling 4 to 5 times during each attack and that anxiety ratings consistently decrease throughout the attack. Thoughts about health were the most common trigger during the observed panic attacks, and potential lifestyle contributors include slightly worse stress, sleep, and eating habits, slightly less exercise, and slightly less drug/alcohol consumption than typical. The pilot study revealed that PanicMechanic is largely feasible to use, but would be made more so with simple modifications to the app and particularly the integration of consumer wearables. Similarly, participants found PanicMechanic useful, with 94% indicating that they would recommend PanicMechanic to a friend. These results point toward the need for future development and a controlled trial to establish effectiveness of this digital therapeutic for preventing panic attacks.

scholarly journals Predicting Student’s Performance Using Machine Learning Algorithm

2021 ◽  
pp. 53-58
Author(s):  
Sheela Rani P ◽  
Dhivya S ◽  
Dharshini Priya M ◽  
Dharmila Chowdary A
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbors

Machine learning is a new analysis discipline that uses knowledge to boost learning, optimizing the training method and developing the atmosphere within which learning happens. There square measure 2 sorts of machine learning approaches like supervised and unsupervised approach that square measure accustomed extract the knowledge that helps the decision-makers in future to require correct intervention. This paper introduces an issue that influences students' tutorial performance prediction model that uses a supervised variety of machine learning algorithms like support vector machine , KNN(k-nearest neighbors), Naïve Bayes and supplying regression and logistic regression. The results supported by various algorithms are compared and it is shown that the support vector machine and Naïve Bayes performs well by achieving improved accuracy as compared to other algorithms. The final prediction model during this paper may have fairly high prediction accuracy .The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features that influence student’s while studying.

scholarly journals Sensor Validation for Indoor Air Quality using Machine Learning

2020 ◽  
Author(s):  
Vagner Seibert ◽  
Ricardo Araújo ◽  
Richard McElligott
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Nearest Neighbor ◽  
Contextual Information ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Sensor Validation ◽  
Single Reading

To guarantee a high indoor air quality is an increasingly important task. Sensors measure pollutants in the air and allow for monitoring and controlling air quality. However, all sensors are susceptible to failures, either permanent or transitory, that can yield incorrect readings. Automatically detecting such faulty readings is therefore crucial to guarantee sensors' reliability. In this paper we evaluate three Machine Learning algorithms applied to the task of classifying a single reading from a sensor as faulty or not, comparing them to standard statistical approaches. We show that all tested machine learning methods -- Multi-layer Perceptron, K-Nearest Neighbor and Random Forest -- outperform their statistical counterparts, both by allowing better separation boundaries and by allowing for the use of contextual information. We further show that this result does not depend on the amount of data, but ML methods are able to continue to improve as more data is made available.

scholarly journals Prediction model for thyrotoxic atrial fibrillation: a retrospective study.

2021 ◽  
Author(s):  
Daria Aleksandrovna Ponomartseva ◽  
Ilia Vladislavovich Derevitskii ◽  
Sergey Valerevich Kovalchuk ◽  
Alina Yurevna Babenko
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Heart Rate ◽  
Retrospective Study ◽  
Prediction Model ◽  
Performance Metrics ◽  
Premature Ventricular Contraction ◽  
Ventricular Contraction ◽  
Atrial Contraction ◽  
Premature Atrial Contraction

Abstract Background: Thyrotoxic atrial fibrillation (TAF) is a recognized significant complication of hyperthyroidism. Early identification of the individuals predisposed to TAF would improve thyrotoxic patients’ management. However, to our knowledge, an instrument that establishes an individual risk of the condition is unavailable. Therefore, the aim of this study is to build a TAF prediction model and rank TAF predictors in order of importance. Methods: In this retrospective study, we have investigated 36 demographic and clinical features for 420 patients with overt hyperthyroidism, 30% of which had TAF. At first, the association of these features with TAF was evaluated by classical statistical methods. Then, we developed several TAF prediction models with eight different machine learning classifiers and compared them by performance metrics. The models included ten features that were selected based on their clinical effectuality and importance for model output. Finally, we ranked TAF predictors, elicited from the optimal final model, by the machine learning tehniques. Results: The best performance metrics prediction model was built with the extreme gradient boosting classifier. It had the reasonable accuracy of 84% and AUROC of 0.89 on the test set. The model confirmed such well-known TAF risk factors as age, sex, hyperthyroidism duration, heart rate and some concomitant cardiovascular diseases (arterial hypertension and conjestive heart rate). We also identified premature atrial contraction and premature ventricular contraction as new TAF predictors. The top five TAF predictors, elicited from the model, included (in order of importance) PAC, PVC, hyperthyroidism duration, heart rate during hyperthyroidism and age. Conclusions: We developed a machine learning model for TAF prediction. It seems to be the first available analytical tool for TAF risk assessment. In addition, we defined five most important TAF predictors, including premature atrial contraction and premature ventricular contraction as the new ones. These results have contributed to TAF prediction investigation and may serve as a basis for further research focused on TAF prediction improvement and facilitation of thyrotoxic patients’ management.

scholarly journals Determination of Significant Features for Building an Efficient Heart Disease Prediction System

2019 ◽  
Vol 8 (2) ◽  
pp. 4499-4504
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Heart Diseases ◽  
Medical Diagnostics ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Prediction System ◽  
Early Stages

Heart diseases are responsible for the greatest number of deaths all over the world. These diseases are usually not detected in early stages as the cost of medical diagnostics is not affordable by a majority of the people. Research has shown that machine learning methods have a great capability to extract valuable information from the medical data. This information is used to build the prediction models which provide cost effective technological aid for a medical practitioner to detect the heart disease in early stages. However, the presence of some irrelevant and redundant features in medical data deteriorates the competence of the prediction system. This research was aimed to improve the accuracy of the existing methods by removing such features. In this study, brute force-based algorithm of feature selection was used to determine relevant significant features. After experimenting rigorously with 7528 possible combinations of features and 5 machine learning algorithms, 8 important features were identified. A prediction model was developed using these significant features. Accuracy of this model is experimentally calculated to be 86.4%which is higher than the results of existing studies. The prediction model proposed in this study shall help in predicting heart disease efficiently.

Software Maintainability: Systematic Literature Review and Current Trends

2016 ◽  
Vol 26 (08) ◽  
pp. 1221-1253 ◽  
Author(s):  
Ruchika Malhotra ◽  
Anuradha Chug
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Evaluation System ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Design Metrics ◽  
Current Trends ◽  
Software Maintainability ◽  
Early Phases

Software maintenance is an expensive activity that consumes a major portion of the cost of the total project. Various activities carried out during maintenance include the addition of new features, deletion of obsolete code, correction of errors, etc. Software maintainability means the ease with which these operations can be carried out. If the maintainability can be measured in early phases of the software development, it helps in better planning and optimum resource utilization. Measurement of design properties such as coupling, cohesion, etc. in early phases of development often leads us to derive the corresponding maintainability with the help of prediction models. In this paper, we performed a systematic review of the existing studies related to software maintainability from January 1991 to October 2015. In total, 96 primary studies were identified out of which 47 studies were from journals, 36 from conference proceedings and 13 from others. All studies were compiled in structured form and analyzed through numerous perspectives such as the use of design metrics, prediction model, tools, data sources, prediction accuracy, etc. According to the review results, we found that the use of machine learning algorithms in predicting maintainability has increased since 2005. The use of evolutionary algorithms has also begun in related sub-fields since 2010. We have observed that design metrics is still the most favored option to capture the characteristics of any given software before deploying it further in prediction model for determining the corresponding software maintainability. A significant increase in the use of public dataset for making the prediction models has also been observed and in this regard two public datasets User Interface Management System (UIMS) and Quality Evaluation System (QUES) proposed by Li and Henry is quite popular among researchers. Although machine learning algorithms are still the most popular methods, however, we suggest that researchers working on software maintainability area should experiment on the use of open source datasets with hybrid algorithms. In this regard, more empirical studies are also required to be conducted on a large number of datasets so that a generalized theory could be made. The current paper will be beneficial for practitioners, researchers and developers as they can use these models and metrics for creating benchmark and standards. Findings of this extensive review would also be useful for novices in the field of software maintainability as it not only provides explicit definitions, but also lays a foundation for further research by providing a quick link to all important studies in the said field. Finally, this study also compiles current trends, emerging sub-fields and identifies various opportunities of future research in the field of software maintainability.

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