Key result
A proposed machine learning system using Random Forest achieved an 85% accuracy rate in predicting abnormal behavior patterns from a FitBit dataset to anticipate potential diseases.
Why the study?
Wearable devices are increasingly prevalent for chronic disease and heart rate monitoring, but methods are needed to predict abnormal behavior patterns to anticipate potential diseases.
Do machine learning models accurately predict abnormal behavior patterns using FitBit data?
Do machine learning models accurately predict abnormal behavior patterns using FitBit data?
A machine learning system using Random Forest achieved 85% accuracy in predicting abnormal behavior patterns from FitBit data, which may aid in disease prediction.
The use of wearable devices in healthcare has become increasingly prevalent, with a focus on monitoring chronic diseases such as heart rate. Researchers have employed various methods, including machine learning, to analyze and predict personal activities. This study aims to predict abnormal behavior patterns within a FitBit dataset to anticipate potential diseases. The methodology involves using classification models such as Long Short-Term Memory (LSTM), Deep Neural network (DNN), and Support Vector Memory (SVM), as well as feature selection techniques such as Recursive Feature Elimination (RFE) and Random Forest (RF). The proposed system achieved a high accuracy rate of 85% using Random Forest. This research can aid in predicting and preventing future diseases, as well as providing behavior advice to help individuals achieve their fitness goals.
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Hayat et al. (2023) studied Abnormal behavior patterns. Machine learning classification models (Random Forest) was evaluated on Accuracy rate in predicting abnormal behavior patterns. A proposed machine learning system using Random Forest achieved an 85% accuracy rate in predicting abnormal behavior patterns from a FitBit dataset to anticipate potential diseases.
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