Predicting Depression Using Social Media Posts

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Fahem Abu Bakar ◽  
◽  
Nazri Mohd Nawi ◽  
Abdulkareem A. Hezam ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Support Vector Machine ◽  
Neural Network Model ◽  
Support Vector ◽  
Learning Models ◽  
The Neural Network ◽  
Machine Learning Models

The use of Social Network Sites (SNS) is on the rise these days, particularly among the younger generations. Users can communicate their interests, feelings, and everyday routines thanks to the availability of social media sites. Many studies show that properly utilizing user-generated content (UGC) can aid in determining people's mental health status. The use of the UGC could aid in the prediction of mental health, particularly depression, where it is a significant medical condition that impairs one's ability to work, learn, eat, sleep, and enjoy life. However, all information about a person's mood and negativism can be gathered from their SNS user profile. Therefore, this study utilizes SNS as a data source by using machine learning models to screen and identify users in categorizing users based on their mental health. The performance of three machine learning models is evaluated to classify the UGC: Decision Forest, Neural Network, and Support Vector Machine (SVM). The results show that the accuracy and recall result of the Neural Network model is the same as the Support Vector Machine (SVM) model, which is 78.27% and 0.042, but Neural Network performs better in the average precision value. This proves that the Neural Network model is the best model for making predictions to determine the level of depression by using social media posts.

scholarly journals Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257069
Author(s):  
Jae-Geum Shim ◽  
Kyoung-Ho Ryu ◽  
Sung Hyun Lee ◽  
Eun-Ah Cho ◽  
Sungho Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Random Forest ◽  
Tracheal Tube ◽  
Pediatric Patients ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

Objective To construct a prediction model for optimal tracheal tube depth in pediatric patients using machine learning. Methods Pediatric patients aged <7 years who received post-operative ventilation after undergoing surgery between January 2015 and December 2018 were investigated in this retrospective study. The optimal location of the tracheal tube was defined as the median of the distance between the upper margin of the first thoracic(T1) vertebral body and the lower margin of the third thoracic(T3) vertebral body. We applied four machine learning models: random forest, elastic net, support vector machine, and artificial neural network and compared their prediction accuracy to three formula-based methods, which were based on age, height, and tracheal tube internal diameter(ID). Results For each method, the percentage with optimal tracheal tube depth predictions in the test set was calculated as follows: 79.0 (95% confidence interval [CI], 73.5 to 83.6) for random forest, 77.4 (95% CI, 71.8 to 82.2; P = 0.719) for elastic net, 77.0 (95% CI, 71.4 to 81.8; P = 0.486) for support vector machine, 76.6 (95% CI, 71.0 to 81.5; P = 1.0) for artificial neural network, 66.9 (95% CI, 60.9 to 72.5; P < 0.001) for the age-based formula, 58.5 (95% CI, 52.3 to 64.4; P< 0.001) for the tube ID-based formula, and 44.4 (95% CI, 38.3 to 50.6; P < 0.001) for the height-based formula. Conclusions In this study, the machine learning models predicted the optimal tracheal tube tip location for pediatric patients more accurately than the formula-based methods. Machine learning models using biometric variables may help clinicians make decisions regarding optimal tracheal tube depth in pediatric patients.

scholarly journals Predicting S&P 500 Market Price by Deep Neural Network and Enemble Model

2020 ◽  
Vol 214 ◽  
pp. 02040
Author(s):  
Feiyu Wang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Linear Regression ◽  
Deep Neural Network ◽  
Market Price ◽  
Support Vector ◽  
Learning Models ◽  
Conventional Machine ◽  
Machine Learning Models

The method to predict the movement of stock market has appealed to scientists for decades. In this article, we use three different models to tackle that problem. In particular, we propose a Deep Neural Network (DNN) to predict the intraday direction of SP500 index and compare the DNN with two conventional machine learning models, i.e. linear regression, support vector machine. We demonstrate that DNN is able to predict SP500 index with relatively highest accuracy.

Machine Learning Models for Finger Bend Evaluation using Implemented Low cost Flex Sensor

2021 ◽  
Vol 9 (VI) ◽  
pp. 3605-3611
Author(s):  
Pratyush Kaware
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Low Cost ◽  
Learning Algorithms ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

In this paper a cost-effective sensor has been implemented to read finger bend signals, by attaching the sensor to a finger, so as to classify them based on the degree of bent as well as the joint about which the finger was being bent. This was done by testing with various machine learning algorithms to get the most accurate and consistent classifier. Finally, we found that Support Vector Machine was the best algorithm suited to classify our data, using we were able predict live state of a finger, i.e., the degree of bent and the joints involved. The live voltage values from the sensor were transmitted using a NodeMCU micro-controller which were converted to digital and uploaded on a database for analysis.

scholarly journals Assessment of Machine Learning Models to Identify Port Jackson Shark Behaviours Using Tri-Axial Accelerometers

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7096
Author(s):  
Julianna P. Kadar ◽  
Monique A. Ladds ◽  
Joanna Day ◽  
Brianne Lyall ◽  
Culum Brown
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Classification Tree ◽  
Support Vector ◽  
Fine Scale ◽  
Learning Models ◽  
Port Jackson ◽  
F Measure ◽  
Machine Learning Models ◽  
Broad Scale

Movement ecology has traditionally focused on the movements of animals over large time scales, but, with advancements in sensor technology, the focus can become increasingly fine scale. Accelerometers are commonly applied to quantify animal behaviours and can elucidate fine-scale (<2 s) behaviours. Machine learning methods are commonly applied to animal accelerometry data; however, they require the trial of multiple methods to find an ideal solution. We used tri-axial accelerometers (10 Hz) to quantify four behaviours in Port Jackson sharks (Heterodontus portusjacksoni): two fine-scale behaviours (<2 s)—(1) vertical swimming and (2) chewing as proxy for foraging, and two broad-scale behaviours (>2 s–mins)—(3) resting and (4) swimming. We used validated data to calculate 66 summary statistics from tri-axial accelerometry and assessed the most important features that allowed for differentiation between the behaviours. One and two second epoch testing sets were created consisting of 10 and 20 samples from each behaviour event, respectively. We developed eight machine learning models to assess their overall accuracy and behaviour-specific accuracy (one classification tree, five ensemble learners and two neural networks). The support vector machine model classified the four behaviours better when using the longer 2 s time epoch (F-measure 89%; macro-averaged F-measure: 90%). Here, we show that this support vector machine (SVM) model can reliably classify both fine- and broad-scale behaviours in Port Jackson sharks.

scholarly journals Fraudulent Face Image Detection

2020 ◽  
Vol 32 ◽  
pp. 03005
Author(s):  
Rahul Awhad ◽  
Saurabh Jayswal ◽  
Adesh More ◽  
Jyoti Kundale
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Face Image ◽  
Support Vector ◽  
Learning Models ◽  
Image Detection ◽  
Software Applications ◽  
Support Vector Classifier ◽  
The Face ◽  
Machine Learning Models

Due to the growing advancements in technology, many software applications are being developed to modify and edit images. Such software can be used to alter images. Nowadays, an altered image is so realistic that it becomes too difficult for a person to identify whether the image is fake or real. Such software applications can be used to alter the image of a person’s face also. So, it becomes very difficult to identify whether the image of the face is real or not. Our proposed system is used to identify whether the image of a face is fake or real. The proposed system makes use of machine learning. The system makes use of a convolution neural network and support vector classifier. Both these machine learning models are trained using real as well as fake images. Both these trained models will take an image as an input and will determine whether the image is fake or real.

scholarly journals Application of Natural Language Processing with Supervised Machine Learning Techniques to Predict the Overall Drugs Performance

2020 ◽  
Vol 11 (40) ◽  
pp. 8-23
Author(s):  
Pius MARTHIN ◽  
Duygu İÇEN
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Semantic Analysis ◽  
Classification Tree ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

Online product reviews have become a valuable source of information which facilitate customer decision with respect to a particular product. With the wealthy information regarding user's satisfaction and experiences about a particular drug, pharmaceutical companies make the use of online drug reviews to improve the quality of their products. Machine learning has enabled scientists to train more efficient models which facilitate decision making in various fields. In this manuscript we applied a drug review dataset used by (Gräβer, Kallumadi, Malberg,& Zaunseder, 2018), available freely from machine learning repository website of the University of California Irvine (UCI) to identify best machine learning model which provide a better prediction of the overall drug performance with respect to users' reviews. Apart from several manipulations done to improve model accuracy, all necessary procedures required for text analysis were followed including text cleaning and transformation of texts to numeric format for easy training machine learning models. Prior to modeling, we obtained overall sentiment scores for the reviews. Customer's reviews were summarized and visualized using a bar plot and word cloud to explore the most frequent terms. Due to scalability issues, we were able to use only the sample of the dataset. We randomly sampled 15000 observations from the 161297 training dataset and 10000 observations were randomly sampled from the 53766 testing dataset. Several machine learning models were trained using 10 folds cross-validation performed under stratified random sampling. The trained models include Classification and Regression Trees (CART), classification tree by C5.0, logistic regression (GLM), Multivariate Adaptive Regression Spline (MARS), Support vector machine (SVM) with both radial and linear kernels and a classification tree using random forest (Random Forest). Model selection was done through a comparison of accuracies and computational efficiency. Support vector machine (SVM) with linear kernel was significantly best with an accuracy of 83% compared to the rest. Using only a small portion of the dataset, we managed to attain reasonable accuracy in our models by applying the TF-IDF transformation and Latent Semantic Analysis (LSA) technique to our TDM.

scholarly journals Support Vector Machine And K-Nearest Neighbor Based Liver Disease Classification Model

2021 ◽  
Vol 3 (1) ◽  
pp. 9-14
Author(s):  
Tsehay Admassu Assegie
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Liver Disease ◽  
Classification Model ◽  
Support Vector ◽  
Disease Prediction ◽  
Accuracy Score ◽  
Learning Models ◽  
Accuracy And Precision ◽  
Machine Learning Models

Machine-learning approaches have become greatly applicable in disease diagnosis and prediction process. This is because of the accuracy and better precision of the machine learning models in disease prediction. However, different machine learning models have different accuracy and precision on disease prediction. Selecting the better model that would result in better disease prediction accuracy and precision is an open research problem. In this study, we have proposed machine learning model for liver disease prediction using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) learning algorithms and we have evaluated the accuracy and precision of the models on liver disease prediction using the Indian liver disease data repository. The analysis of result showed 82.90% accuracy for SVM and 72.64% accuracy for the KNN algorithm. Based on the accuracy score of SVM and KNN on experimental test results, the SVM is better in performance on the liver disease prediction than the KNN algorithm.  

scholarly journals A Comparative Analysis of Machine Learning Algorithms to Predict Alzheimer’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Morshedul Bari Antor ◽  
A. H. M. Shafayet Jamil ◽  
Maliha Mamtaz ◽  
Mohammad Monirujjaman Khan ◽  
Sultan Aljahdali ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Machine Learning Algorithms ◽  
Learning System ◽  
Fine Tuning ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

Alzheimer’s disease has been one of the major concerns recently. Around 45 million people are suffering from this disease. Alzheimer’s is a degenerative brain disease with an unspecified cause and pathogenesis which primarily affects older people. The main cause of Alzheimer’s disease is Dementia, which progressively damages the brain cells. People lost their thinking ability, reading ability, and many more from this disease. A machine learning system can reduce this problem by predicting the disease. The main aim is to recognize Dementia among various patients. This paper represents the result and analysis regarding detecting Dementia from various machine learning models. The Open Access Series of Imaging Studies (OASIS) dataset has been used for the development of the system. The dataset is small, but it has some significant values. The dataset has been analyzed and applied in several machine learning models. Support vector machine, logistic regression, decision tree, and random forest have been used for prediction. First, the system has been run without fine-tuning and then with fine-tuning. Comparing the results, it is found that the support vector machine provides the best results among the models. It has the best accuracy in detecting Dementia among numerous patients. The system is simple and can easily help people by detecting Dementia among them.

scholarly journals COVID-19 Future Predictions Using 4 Supervised Machine Learning Models

2022 ◽  
pp. 61-66
Author(s):  
Aditi Vadhavkar ◽  
Pratiksha Thombare ◽  
Priyanka Bhalerao ◽  
Utkarsha Auti
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Support Vector Machine ◽  
Supervised Machine Learning ◽  
Perioperative Outcomes ◽  
Support Vector ◽  
Learning Models ◽  
Death Rates ◽  
The World ◽  
Machine Learning Models

Forecasting Mechanisms like Machine Learning (ML) models having been proving their significance to anticipate perioperative outcomes in the domain of decision making on the future course of actions. Many application domains have witnessed the use of ML models for identification and prioritization of adverse factors for a threat. The spread of COVID-19 has proven to be a great threat to a mankind announcing it a worldwide pandemic throughout. Many assets throughout the world has faced enormous infectivity and contagiousness of this illness. To look at the figure of undermining components of COVID-19 we’ve specifically used four Machine Learning Models Linear Regression (LR), Least shrinkage and determination administrator (LASSO), Support vector machine (SVM) and Exponential smoothing (ES). The results depict that the ES performs best among the four models employed in this study, followed by LR and LASSO which performs well in forecasting the newly confirmed cases, death rates yet recovery rates, but SVM performs poorly all told the prediction scenarios given the available dataset.

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