scholarly journals Human activity recognition based on machine learning classification of smartwatch accelerometer dataset

2021 ◽  
Vol 49 (1) ◽  
pp. 225-232
Author(s):  
Dušan Radivojević ◽  
Nikola Mirkov ◽  
Slobodan Maletić
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Time Series Data ◽  
Test Group ◽  
Wearable Devices ◽  
Random Forest Classifier ◽  
Series Data ◽  
Machine Learning Classification ◽  
Improving Accuracy

This paper presents two Machine Learning models that classify time series data given from smartwatch accelerometer of observed subjects. For the purpose of classification we use Deep Neural Network and Random Forest classifier algorithms. The comparison of both models shows that they have similar performance with regard to recognition of subject's activities that are used in the test group of the dataset. Training accuracy reaches approximately 95% and 100% for Deep Learning and Random Forest model respectively. Since the validation and recognition, reached about 81% and 75% respectively, a tendency for improving accuracy as a function of number of participants is considered. The influence of data sample precision to the accuracy of the models is examined since the input data could be given from various wearable devices.

scholarly journals Iterative Update of a Random Forest Classifier for Diabetic Retinopathy

2021 ◽  
Author(s):  
Jordi Pascual-Fontanilles ◽  
Aida Valls ◽  
Antonio Moreno ◽  
Pedro Romero-Aroca
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Random Forest ◽  
Random Forests ◽  
Random Forest Classifier ◽  
Diabetic Patients ◽  
Similar Data ◽  
Machine Learning Classification ◽  
Inherent Ambiguity ◽  
New Iterative Method

Random Forests are well-known Machine Learning classification mechanisms based on a collection of decision trees. In the last years, they have been applied to assess the risk of diabetic patients to develop Diabetic Retinopathy. The results have been good, despite the unbalance of data between classes and the inherent ambiguity of the problem (patients with similar data may belong to different classes). In this work we propose a new iterative method to update the set of trees in the Random Forest by considering trees generated from the data of the new patients that are visited in the medical centre. With this method, it has been possible to improve the results obtained with standard Random Forests.

scholarly journals Land Cover Classification of an Area Susceptible to Landslides Using Random Forest and NDVI Time Series Data

2020 ◽  
Author(s):  
Tatiana Dias Tardelli Uehara ◽  
Anderson Reis Soares ◽  
Renata Pacheco Quevedo ◽  
Thales Sehn Korting ◽  
Leila Maria Garcia Fonseca ◽  
...  
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Land Cover ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Series Data ◽  
Ndvi Time Series

scholarly journals Predicting stroke risk by Migraine using AI

2021 ◽  
pp. 285-290
Author(s):  
Anchal Singh ◽  
Dr. Surabhi Thorat
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Early Stage ◽  
Blood Clot ◽  
Random Forest Classifier ◽  
Machine Learning Algorithms ◽  
Machine Learning Classification ◽  
Life Threatening ◽  
Confusion Matrices ◽  
The Brain

Stroke is a blood clot or bleeds in the brain, which can make permanent damage that has an effect on mobility, cognition, sight or communication. It is the second leading cause of death worldwide and one of the most life- threatening diseases for persons above 65 years. It damages the brain like “heart attack” which damages the heart. Every 4 minutes someone dies of stroke, but up to 80% of stroke can be prevented if we can identify or predict the occurrence of stroke in its early stage. In this paper, I used different types of machine learning algorithms for stroke prediction on the Healthcare Dataset Stroke data. Four types of machine learning classification algorithms were applied; Linear Regression, Confusion matrices, Random Forest Classifier, and Logistic Regression were used to build the stroke prediction model. Support, Precision, Recall, and F1-score were used to calculate performance measures of machine learning models. The results showed that Random Forest Classifier has achieved the best accuracy at 94 % [1].

scholarly journals Interrogation of Sentiment Perusing with Hash Counting Vectorizer and Term Inverse Frequency Transformer using Machine Learning Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 3895-3901
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Customer Satisfaction ◽  
Current Trend ◽  
Random Forest Classifier ◽  
Gradient Boosting ◽  
Svm Classifier ◽  
Bayes Classifier ◽  
Machine Learning Classification ◽  
Tree Classifier

With the fast growing technology, the business is moving towards increasing their profit by interpreting the customer satisfaction. The customer satisfaction can be analyzed in many ways. It is the responsibility of the business to analyze the customer satisfaction in order to improve their turnover and profit. With the current trend, the customers are giving their feedback through mobile and internet. With this overview, this paper attempts to analyze the sentiment of the customer feedback for the movie. The sentiment Analysis on movie Review dataset from the KAGGLE Machine learning repository is used for implementation. The type of sentiment classes is predicted through the following ways. Firstly, the sentiment count for each class is displayed and the top feature words for each sentiment class are also extracted from the dataset. Secondly, the dataset is sampled with counting vectorizer and then fitted with the classifiers like Logistic Regression Classifier, Linear SVM Classifier, Multinomial Naives Bayes Classifier, Gradient Boosting Classifer, Guassian Naive Bayes Classifier, Random Forest Classifier, Decision Tree Classifier and and Extra Tree Classifier. Thirdly, the dataset is sampled with Hashing vectorizer and then fitted with the above specified classifiers. Fourth, the dataset is sampled with TFIFD vectorizer and then fitted with the above specified classifiers. Fifth, the dataset is sampled with TFIFD Transformer and then fitted with the above specified classifiers. Sixth, the Performance analysis of classifiers is performed by analyzing the metrics like Precision, Recall, Fscore and Accuracy. The implementation is carried out using python code in Spyder Anaconda Navigator IP Console. Experimental results shows that the analysis of sentiment done by the random forest classifier is found to be more effective with the Accuracy of 89% for Counting vectorizer and TFIFD transformer, Accuracy of 87% for Hashing vectorizer and Accuracy of 88% for TFIFD vectorizer.

scholarly journals Machine Learning Models with Time-Series Clinical Features to Predict Radiographic Progression in Patients with Ankylosing Spondylitis

2021 ◽  
Author(s):  
Bon San Koo ◽  
Miso Jang ◽  
Ji Seon Oh ◽  
Keewon Shin ◽  
Seunghun Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Ankylosing Spondylitis ◽  
Random Forest ◽  
Clinical Features ◽  
Radiographic Progression ◽  
Time Series Data ◽  
Series Data ◽  
Learning Models ◽  
Machine Learning Models

Abstract Background: Radiographic progression in patients with ankylosing spondylitis (AS) varies between individuals, and its evaluation requires a long period of time. Previous statistical studies for radiographic progression have limitations in integrating and analyzing multiple variables of various types. The purpose of this study was to establish the application of machine learning models for predicting radiographic progression in patients with AS using time-series data from electronic medical records (EMRs).Methods: EMR data, including baseline characteristics, laboratory finding, drug administration, and modified Stoke Ankylosing Spondylitis Spine Score (mSASSS), were collected from 1,123 AS patients who were followed up for 18 years at a common center at the time of first (T1), second (T2), and third (T3) visits. The radiographic progression of the (n + 1)th visit (Pn+1 = (mSASSSn+1 – mSASSSn) / (Tn+1 – Tn) ≥ 1 unit per year) was predicted using follow-up visit datasets from T1 to Tn. Three machine learning methods (logistic regression with least absolute shrinkage and selection operation, random forest, and extreme gradient boosting algorithms) with three-fold cross validation were used. Results: The random forest model using the T1 EMR dataset showed the highest performance in predicting the radioactive progression P2 among all the machine learning models tested. The mean accuracy and the area under the curves were 73.73% and 0.79, respectively. Among the variables of T1, the most important variables for predicting radiographic progression were in the order of total mSASSS, age, and alkaline phosphatase. Conclusion: Prognosis predictive models using time-series data showed reasonable performance with clinical features of the first visit dataset for predicting radiographic progression. Additional feature data such as spine radiographs or life-log data may improve the performance of these models.

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