Multivariate time-series sensor vital sign forecasting of cardiovascular and chronic respiratory diseases

• Published in: Sustainable computing : informatics and systems Vol. 38; p. 100868
• Main Authors: Ahmed, Usman, Lin, Jerry Chun-Wei, Srivastava, Gautam
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2023
• Subjects: Cardiovascular disease; Chronic respiratory disease; Machine learning; Sensor data; Cardiovascular disease; Sensor data; Machine learning; Chronic respiratory disease
• ISSN: 2210-5379; 2210-5387; 2210-5379
• DOI: 10.1016/j.suscom.2023.100868

Approximately 19 million people die each year from cardiovascular and chronic respiratory diseases. As a result of the recent Covid-19 epidemic, blood pressure, cholesterol, and blood sugar levels have risen. Not only do healthcare institutions benefit from studying physiological vital signs, but individuals also benefit from being alerted to health problems in a timely manner. This study uses machine learning to categorize and predict cardiovascular and chronic respiratory diseases. By predicting a patient’s health status, caregivers and medical professionals can be alerted when needed. We predicted vital signs for 180 seconds using real-world vital sign data. A person’s life can be saved if caregivers react quickly and anticipate emergencies. The tree-based pipeline optimization method (TPOT) is used instead of manually adjusting machine learning classifiers. This paper focuses on optimizing classification accuracy by combining feature pre-processors and machine learning models with TPOT genetic programming making use of linear and Prophet models to predict important indicators. The TPOT tuning parameter combines predicted values with classical classification models such as Naïve Bayes, Support Vector Machines, and Random Forests. As a result of this study, we show the importance of categorizing and increasing the accuracy of predictions. The proposed model achieves its adaptive behavior by conceptually incorporating different machine learning classifiers. We compare the proposed model with several state-of-the-art algorithms using a large amount of training data. Test results at the University of Queensland using 32 patient’s data showed that the proposed model outperformed existing algorithms, improving the classification of cardiovascular disease from 0.58 to 0.71 and chronic respiratory disease from 0.49 to 0.70, respectively, while minimizing the mean percent error in vital signs. Our results suggest that the Facebook Prophet prediction model in conjunction with the TPOT classification model can correctly diagnose a patient’s health status based on abnormal vital signs and enables patients to receive prompt medical attention. •Propose an in-depth analysis of the sensors dataset used in the designed model.•Develop the model for chronic respiratory and cardiovascular disease.•Results showed that the designed model outperforms the SOTA approaches.