are feature labels, described in Table 6. Various techniques can be used to perform correlation analysis to identify relationships in the dataset. To quantify the association between two variables you can calculate the correlation coefficient. The first value in Figure 5 is the accuracy of the learning algorithm when all 24 features are selected, and the last value shows the accuracy when the top four features are selected from the ranked list in Table 11. Based on the selected attribute set, the machine learning model is trained and evaluated in terms of prediction accuracy. Feature labels are used as a short form, so that it can be referred in the rest of the paper. The training subsets allows for data with replacement. PDF Chapter 6: Data Analysis and Interpretation 6.1. Introduction How to measure the relationship between variables patterns that may include interactions between variables, as well as interactions within subsets of variables. Dev is the standard deviation. Their input data included the physiological readings of galvanic skin response (GSR), respiration, blood pressure, and electroencephalography (EEG). Further, the study found that high temperature and long hours of daylight had an inverse relationship with depression score, hence they were considered to be the best predictors of depression among the parameters. Jarwar M.A., Abbasi R.A., Mushtaq A., Maqbool O., Aljohani N.R., Daud A., Alowibdi J.S., Cano J.R., Garca S., Chong I. Undefined CommuniMents: A Framework for Detecting Community Based Sentiments for Events. Higher correlations between the features and the dependent class indicate higher correlation between the feature set and dependent class. Identify significant relationships between variables What kind of data do you have? Basic statistical tools in research and data analysis - PMC This indicates that each sensor data has unique patterns, which can be an important factor in identifying emotional states. In this work, we have both quantitative and categorical data considering multimodal data. Zong S. A study on adolescent suicide ideation in South Korea. This dataset is used separately for Bipolar and Melancholia disorder, with their respective depressive disorder scores. 1.3.2, p. 12). This signal can be a good source for detecting anger or romantic love, as these emotions can cause skin to sweat, and hence can be identified by GSR sensor. Selection bias in gene extraction on the basis of microarray gene-expression data. Also in other studies, the season was observed as important parameter [17,18], specifically considering peaks of summer and winter. The goal of this approach is to reduce the variance in the result (over fitting). Another challenge is identifying prominent and persistent relationships over longer periods of time. Table 10 shows the correlation among the extracted features. This dataset consists of the physiological response data of a subject with four sensors attached to his body. Min Value and Max Value are the Minimum and Maximum values in the dataset. The symptom data is collected after every second day and weather data has been considered for the same day. Table 4 shows the profile information of the dataset used for the analysis of depressive disorder. Zeng L.-L., Shen H., Liu L., Wang L., Li B., Fang P., Zhou Z., Li Y., Hu D. Identifying major depression using whole-brain functional connectivity: A multivariate pattern analysis. The strength of correlation can be related to the results in Table 8. In this regard, the training set consists of 66% of the total dataset, whereas the remaining 34% has been used for evaluations. Regression analysis is used to estimate the relationship between a set of variables. For predicting the depressive disorder severity levels based on the weather dataset, four classification techniques have been applied as described in Section 3.3. Ambroise C., McLachlan G.J. From these results, it can be concluded that the nine top-ranked features from Table 11 provide the optimum results in the case of using Random Forest as the classification model. Correlation analysis in research is a statistical method used to measure the strength of the linear relationship between two variables and compute their association. Does the weather make us sad? Further, additional mechanisms are required to identify the combined effect of independent parameters on target classes. These algorithms have been applied without any modification in the core development of the WEKA tool. Bethesda, MD 20894, Web Policies The WEKA Workbench. The ranking of extracted features has been performed using Weka, based on the approach described in Section 3.2. The prediction model is then imported in the prediction server, so that it is able to predict based on the incoming requests. Because samples tend to be large, data analysis is typically conducted through the use of . . 311313. Analyze and interpret data to provide evidence for phenomena. It then performs calculations for depressive disorder severity. Here it can be seen that, as the weak predictors are removed, the overall accuracies of the algorithms increase until around 4050% of predictor attributes remain. In this figure, the hypothesis emphasizes that adding features (weather parameters/physiological signal data) that have high correlation coefficients will have a positive effect on the models accuracy, and vice versa. In this scale, anger is labeled as ANGRY, contempt as contemptuous, disgust as disgusted, fear as AFRAID, happiness as HAPPY, sadness as SAD, and surprise as ASTONISHED. Thesescalesarenominal, ordinalandnumerical. Syllignakis M.N., Kouretas G.P. Hence, the sad emotion score is used to evaluate the depression severity level. The proposed system will be used to enhance decision-making capabilities at the Service level, which will further support applications such as forecasting, emergency detection, and notification and recommendation services [57,58,59]. In case of Ozone, we cannot be certain of this relationship as the data scatter shows signs of heteroscedasticity. Wagner J., Kim J., Andre E. From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification; Proceedings of the 2005 IEEE International Conference on Multimedia and Expo; Amsterdam, The Netherlands. We have considered three types of data for the two depressive disorder cases listed above. The respiration signals are conducted through quantifying inhalation and exhalation based on chest cavity expansion and contraction, respectively. Smart spaces recommending service provisioning in WoO platform; Proceedings of the 2017 International Conference on Information and Communications (ICIC); Hanoi, Vietnam. Spasova Z. The effect of temperature on depression was also identified by Molin et al. Section 6 discusses the interpretation of results as well as provides limitations and possible direction of this research in the future works. The basic syntax is cor.test (var1, var2, method = "method"), with the default method being pearson. [23] used multi regression model to analysis effect of weather on the patients energy levels and sleep. Introduction to Exploratory Data Analysis (EDA) The highlighted Spearmans correlation shows that it considers only ordinal data in the categorical dataset type. For example, Lift is one correlation measure with a coefficient ranging around one. According to the results shown by Picard et al. A Correlation Analysis of Web Social Media; Proceedings of the International Conference on Web Intelligence, Mining and Semantics; Sogndal, Norway. One for emotion detection application and the other is for depressive disorder situation analysis based on weather. official website and that any information you provide is encrypted and transmitted securely. For the machine learning and prediction, the Weka machine learning tool is used in the java platform. One of the factor can be the lack of similarity in the dataset as compared to the one acquired from real test subject. With these observations, we have evaluated temperature, atmospheric pressure, and ozone to be the strong predictors for depression severity. Data in categories (nominal, ordinal) Ordinal, rank-order, or non-normal scale data Scale, numeric data (interval, ratio) Ordinal dependent and scale or categorical independent variables The physiological signals involve: electromyogram (EMG), blood volume pulse (BVP), galvanic skin response (GSR), and respiration (Resp). Storm and Humidity are among the bottom-ranked factors in Bipolar and Melancholia disorder, respectively. Therefore, based on the above equation, a feature set can be evaluated based on the following factors: Based on this ranking technique, feature selection has been performed using Weka. The GPS coordinate system has been used instead of address (city, country etc.) Having 24 features makes the correlation table very large, therefore we have selected and shown only the highly-correlated parameters. 112117. Korea is one of those countries where rain is often observed throughout the year, but even then one year is not sufficient for expecting steady trends. Exploratory data analysis is a technique that data scientists and other data professionals use to understand datasets before modeling them. International Journal of Environmental Research and Public Health, http://creativecommons.org/licenses/by/4.0/, http://ailab.ist.psu.edu/yasser/wlsvm.html, https://www.helpguide.org/articles/bipolar-disorder/bipolar-disorder-signs-and-symptoms.htm, https://www.healthline.com/health/depression/melancholic-depression#symptoms, https://www.aaai.org/ocs/index.php/WS/AAAIW14/paper/viewFile/8850/8303, https://www.ijcaonline.org/archives/volume57/number5/9108-3258, https://en.tutiempo.net/climate/ws-471190.html, Pearson product moment correlation, Spearmans Correlation, Elevated feelings and energy for activity. For depression severity evaluation based on emotion score, we have referred to the valence and arousal scale described by Scherer et al. Extracting important variables and leaving behind useless variables; Identifying outliers, missing values, or human error; Understanding the relationship . Prevalence of Depressive Symptoms and Related Factors in Korean Employees: The Third Korean Working Conditions Survey (2011). LibSVM (WLSVM): Integrating LibSVM into Weka Environment. Experimentation for data acquisition and analysis. The expected results may also have some uncertain discovered trend or some unidentified relationship due to the limited observations in datasets. This area can monitor steady breathing and can omit gas flow due to other activities such as talking, etc. ; Project administration, I.C. The five-factor model of the Positive and Negative Syndrome Scale II: A ten-fold cross-validation of a revised model. Two data analysis techniques for quantitative data are regression analysis (which examines relationships between two variables) and hypothesis analysis (which tests whether a hypothesis is true). how to describe the distribution of single variables and the relationships among variables. In order to identify the strong predictor attributes for depressive disorder, the Pearsons correlation-based feature selection technique is used as described in Section 3. Both the BVP and GSR sensors are attached on the left hand of the subjects body. It is a statistical analysis of the collinear relationship between two variables. Feature selection technique also plays an important role in filtering out non-relevant features extracted from physiological signals. An association rule will be considered strong if its support count and confidence are above some defined threshold. Effect of daily variation in weather and sleep on seasonal affective disorder. A statistical model can provide intuitive visualizations that aid data scientists in identifying relationships between variables and making predictions by applying statistical models to raw data. Consider two variables A and B. Neuro-Psychopharmacol. Implementation model of WoO based smart assisted living IoT service; Proceedings of the 2016 International Conference on Information and Communication Technology Convergence (ICTC); Jeju Island, Korea. Ali S., Kim H.-S., Chong I. Paul et al. The unit of measure is the percentage stretch per seconds. Other aspects such as assumptions and hypothesis also vary from one algorithm to another. Sometimes, when you analyze data with correlation and linear regression, you notice that the relationship between the independent ( X) variable and dependent ( Y) variable looks like it follows a curved line, not a straight line. Emergency detection will involve suicide detection, triggers that contribute to escalating depression, notifying concerned doctors and relatives, etc. The Logit boosting algorithm [37] adopts similar techniques as the Adaboost algorithm (Adapting boosting), by minimizing the logistic loss function of logistic regression in each iteration. Hence it can be considered that these attributes have least or no relationship with respect to this dataset. This process is in compliance with existing works [28,29,30,31]. Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. sharing sensitive information, make sure youre on a federal The findings of this analysis did not show any significant effects of environmental factors on mood and depressive disorder. 8.4 (1) https://webmada.csg.uzh.ch)) is used to maintain and access WSNs after successful authentication. Abhang P.A., Gawali B.W. However, each algorithm has its own considerations as well as limitations in identifying the relationships. Then the Pearsons correlation coefficient can be calculated using the following formula: where CA,B is the correlation coefficient, Covariance(A,B) is the covariance, and A and B are the standard deviations of A and B, respectively. Figure 2 shows the steps to be performed. 5.3: Curvilinear (Nonlinear) Regression - Statistics LibreTexts [(accessed on 4 December 2018)]; Recognition of Emotional and Cognitive State Using Physiological Data. Burns M.N., Begale M., Duffecy J., Gergle D., Karr C.J., Giangrande E., Mohr D.C. As there can be different levels of uncertainty in the patterns of acquired data (such as high and low levels of mood in bipolar disorder), longer periods of data are required to analyze the trends.
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