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Paper Type

Complete

Paper Number

1535

Description

Long-term diseases often develop at a slow rate, making it difficult to detect them. These medical conditions often arise due to approached health behavior patterns. The circumstance of negative behavioral patterns encouraging diseases results in treatment issues due to late treatment initialization. This paper presents a machine learning-based approach to predict people’s health behavior tendencies at an early stage based on their Future Time Perspective using objective resting-state EEG data. With a balanced accuracy of 95.00 percent based on the EEG frequency bands identified as relevant (3.5 Hz-4.5 Hz, 4.5 Hz-5.5 Hz, 20 Hz-21 Hz, and 27 Hz-28 Hz), our model sets a first benchmark in this field, building a base for early recognition and intervention of potential long-term diseases.

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Aug 10th, 12:00 AM

Machine Learning-Based Health Behavior Prediction Using Resting-State EEG Data

Long-term diseases often develop at a slow rate, making it difficult to detect them. These medical conditions often arise due to approached health behavior patterns. The circumstance of negative behavioral patterns encouraging diseases results in treatment issues due to late treatment initialization. This paper presents a machine learning-based approach to predict people’s health behavior tendencies at an early stage based on their Future Time Perspective using objective resting-state EEG data. With a balanced accuracy of 95.00 percent based on the EEG frequency bands identified as relevant (3.5 Hz-4.5 Hz, 4.5 Hz-5.5 Hz, 20 Hz-21 Hz, and 27 Hz-28 Hz), our model sets a first benchmark in this field, building a base for early recognition and intervention of potential long-term diseases.

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