Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data

BACKGROUND Feelings of loneliness are associated with poor physical and mental health. Detection of loneliness through passive sensing on personal devices can lead to the development of interventions aimed at decreasing rates of loneliness. OBJECTIVE The aim of this study was to explore the potential of using passive sensing to infer levels of loneliness and to identify the corresponding behavioral patterns. METHODS Data were collected from smartphones and Fitbits (Flex 2) of 160 college students over a semester. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire at the beginning and end of the semester. For a classification purpose, the scores were categorized into high (questionnaire score>40) and low (≤40) levels of loneliness. Daily features were extracted from both devices to capture activity and mobility, communication and phone usage, and sleep behaviors. The features were then averaged to generate semester-level features. We used 3 analytic methods: (1) statistical analysis to provide an overview of loneliness in college students, (2) data mining using the Apriori algorithm to extract behavior patterns associated with loneliness, and (3) machine learning classification to infer the level of loneliness and the change in levels of loneliness using an ensemble of gradient boosting and logistic regression algorithms with feature selection in a leave-one-student-out cross-validation manner. RESULTS The average loneliness score from the presurveys and postsurveys was above 43 (presurvey SD 9.4 and postsurvey SD 10.4), and the majority of participants fell into the high loneliness category (scores above 40) with 63.8% (102/160) in the presurvey and 58.8% (94/160) in the postsurvey. Scores greater than 1 standard deviation above the mean were observed in 12.5% (20/160) of the participants in both pre- and postsurvey scores. The majority of scores, however, fell between 1 standard deviation below and above the mean (pre=66.9% [107/160] and post=73.1% [117/160]). Our machine learning pipeline achieved an accuracy of 80.2% in detecting the binary level of loneliness and an 88.4% accuracy in detecting change in the loneliness level. The mining of associations between classifier-selected behavioral features and loneliness indicated that compared with students with low loneliness, students with high levels of loneliness were spending less time outside of campus during evening hours on weekends and spending less time in places for social events in the evening on weekdays (support=17% and confidence=92%). The analysis also indicated that more activity and less sedentary behavior, especially in the evening, was associated with a decrease in levels of loneliness from the beginning of the semester to the end of it (support=31% and confidence=92%). CONCLUSIONS Passive sensing has the potential for detecting loneliness in college students and identifying the associated behavioral patterns. These findings highlight intervention opportunities through mobile technology to reduce the impact of loneliness on individuals’ health and well-being.

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Use of statistical analysis, data mining, decision analysis and cost effectiveness analysis to analyze medical data : application to comparative effectiveness of lumpectomy and mastectomy for breast cancer.

10.18297/etd/1473 ◽

2011 ◽

Author(s):

Beatrice Ugiliweneza

Keyword(s):

Breast Cancer ◽

Data Mining ◽

Statistical Analysis ◽

Cost Effectiveness ◽

Decision Analysis ◽

Comparative Effectiveness ◽

Analysis Data ◽

Medical Data ◽

Effectiveness Analysis ◽

Data Application

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Cross-Device Computation Coordination for Mobile Collocated Interactions with Wearables

Sensors ◽

10.3390/s19040796 ◽

2019 ◽

Vol 19 (4) ◽

pp. 796 ◽

Cited By ~ 1

Author(s):

Hyoseok Yoon ◽

Choonsung Shin

Keyword(s):

Machine Learning ◽

Data Mining ◽

Big Data ◽

Energy Efficient ◽

Form Factors ◽

Analysis Data ◽

Big Data Analysis ◽

Mining Machine ◽

Coordination Problem ◽

Multiple Devices

Mobile devices, wearables and Internet-of-Things are crammed into smaller form factors and batteries, yet they encounter demanding applications such as big data analysis, data mining, machine learning, augmented reality and virtual reality. To meet such high demands in the multi-device ecology, multiple devices should communicate collectively to share computation burdens and stay energy-efficient. In this paper, we present a cross-device computation coordination method for scenarios of mobile collocated interactions with wearables. We formally define a cross-device computation coordination problem and propose a method for solving this problem. Lastly, we demonstrate the feasibility of our approach through experiments and exemplar cases using 12 commercial Android devices with varying computation capabilities.

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Data Mining Using $\mathcal{MLC}++$ a Machine Learning Library in C++

International Journal of Artificial Intelligence Tools ◽

10.1142/s021821309700027x ◽

1997 ◽

Vol 06 (04) ◽

pp. 537-566 ◽

Cited By ~ 69

Author(s):

Ron Kohavi ◽

Dan Sommerfield ◽

James Dougherty

Keyword(s):

Machine Learning ◽

Data Mining ◽

Pattern Recognition ◽

Statistical Analysis ◽

Classification Algorithms ◽

Pattern Recognition Techniques ◽

Data Mining Algorithms ◽

Multiple Classification ◽

Mining Algorithms ◽

New Algorithms

Data mining algorithms including maching learning, statistical analysis, and pattern recognition techniques can greatly improve our understanding of data warehouses that are now becoming more widespread. In this paper, we focus on classification algorithms and review the need for multiple classification algorithms. We describe a system called [Formula: see text], which was designed to help choose the appropriate classification algorithm for a given dataset by making it easy to compare the utility of different algorithms on a specific dataset of interest. [Formula: see text] not only provides a workbench for such comparisons, but also provides a library of C++ classes to aid in the development of new algorithms, especially hybrid algorithms and multi-strategy algorithms. Such algorithms are generally hard to code from scratch. We discuss design issues, interfaces to other programs, and visualization of the resulting classifiers.

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Machine Learning Framework to Analyze Against Spear Phishing

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l3802.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 3605-3611

Keyword(s):

Machine Learning ◽

Statistical Analysis ◽

Learning Algorithms ◽

Ensemble Methods ◽

Analysis Data ◽

Graphical Analysis ◽

Machine Learning Algorithms ◽

Advanced Persistent Threat ◽

Spam Filter ◽

Algorithm Comparison

The objective of this paper is to design and implement machine learning based ensemble algorithm on dataset to fit into the models that can be understood and executed by machines. In this paper we discussed different algorithms and machine learning concepts that can be implemented on the datasets, we taken email spam filter dataset for experiment and analysis, as the Advanced persistent threat the latest threat is intruded using the emails and major intrusion is done through spam emails. Machine learning uses different datamining techniques and mechanisms and accepts the input-data and gives the output as the statistical analysis. We implemented different email classification algorithms on the datasets based on spam and ham emails where spear phishing methods are identified and implemented different classification and regression methods to get the accurate results. In this paper for the better results in spite of existing algorithms we introduced the ensemble methods such as boosting, bagging, stacking and voting for much accuracy and higher level of classification and combining different algorithm. This paper will measure different machine learning algorithms performance on spam email filtering on the huge datasets. The framework provides implementation of learning algorithms that you can apply to larger datasets. An obvious approach to making decisions more reliable is to combine the output of different models. We even compared the existing algorithms and proposed algorithm; comparison tables are drawn along with the statistical analysis, data and graphical analysis is given.

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An Overview of Data Mining Techniques and Applications and its Future Scope

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset1841079 ◽

2018 ◽

pp. 16-24

Author(s):

Nithya C ◽

Saravanan V

Keyword(s):

Machine Learning ◽

Data Mining ◽

Statistical Analysis ◽

Knowledge Discovery ◽

Mining Technology ◽

Data Mining Techniques ◽

Modeling Techniques ◽

Database Technology ◽

Mining Methods

Data mining is a process which finds useful patterns from large amount of data. The paper discusses few of the data mining techniques, algorithms and some of the organizations which have adapted data mining technology to improve their businesses and found excellent results. The greater part of data mining methods can manage distinctive information sorts.Data mining may be defined as the science of extracting useful information from databases. It also called knowledge discovery. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future.

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