Machine Learning for Healthcare Applications. Группа авторов
Чтение книги онлайн.
Читать онлайн книгу Machine Learning for Healthcare Applications - Группа авторов страница 21
Table 2.5 F1-score of the model.
Health status | Model 1 | Model 2 | ||
---|---|---|---|---|
F1-score:Phase-I | F1-score:Phase-II | F1-score:Phase-I | F1-score:Phase-II | |
Sleep | 94.50549 | 96.25668 | 95.08197 | 96.84211 |
Smoke | 95.69892 | 96.84211 | 96.84211 | 97.89474 |
Drink | 94.50549 | 97.3545 | 97.89474 | 98.94737 |
Screen | 96.17486 | 96.80851 | 96.77419 | 97.3545 |
Calories | 96.80851 | 98.4456 | 97.3262 | 98.96907 |
Figure 2.6 Recall: Model-I vs Model-II.
2.6 Conclusion
In this chapter, we have proposed an architecture based on machine learning algorithms. Basically, we focus on a challenging problem of predicting the overall health status of an individual based on their daily life activities and measures. The proposed system predicts the overall health status of a person and future diseases using machine learning techniques. To demonstrate the proposed model, we have created a web-based application. The proposed model helps the user to understand their health status by submitting their details. For training and testing we used the synthetic data, in the future we need to test the proposed model using the real data by collecting from the users. In this work, we attempted a general healthcare problem and a lot more has to be done in the future. The future work is to predict the diseases based on the overall health status estimation using the models proposed in this chapter.
References
1. Bjartveit, K. and Tverdal, A., Health consequences of smoking 1–4 cigarettes per day. Tobacco Control, 14, 5, 315–320, 2005.
2. Weng, C.-H., Huang, T.C.-K., Han, R.-P., Disease prediction with different types of neural network classifiers. Telematics Inf., 33, 2, 277–292, 2016.
3. Alpaydın, E., Introduction to Machine Learning, 2nd edition, the MIT press, Cambridge, Massachusetts, 2010.
4. Harris, J.A. and Benedict, F.G., A Biometric Study of Human Basal Metabolism. Proc. Natl. Acad. Sci. U.S.A., 4, 12, 370–373, 1918.
5. Hirshkowitz, M., Whiton, K., Albert, S.M., Alessi, C., Bruni, O., DonCarlos, L., Hillard, P.J.A., National Sleep Foundation’s sleep time duration recommendations: Methodology and results summary. Sleep Health, 1, 1, 40–43, 2015.
6. Hung, C.-Y., Chen, W.-C., Lai, P.-T., Lin, C.-H., Lee, C.-C., Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database. 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3110–3113, 2017.
7. Nie, L., Wang, M., Zhang, L., Yan, S., Zhang, B., Chua, T.S., Disease inference from health-related questions via sparse deep learning. IEEE Trans. Knowl. Data Eng., 27, 8, 2107–2119, 2015.
8. Chen, M., Ma, Y., Song, J., Lai, C., Hu, B., Smart clothing: Connecting human with clouds and big data for sustainable health monitoring, in: ACM/Springer Mobile Networks and Applications Mobile, vol. 21, 5, pp. 825–845, 2016.
9. Chen, M., Hao, Y., Hwang, K., Wang, L., Wang, L., Disease Prediction by Machine Learning over Big Data from Healthcare Communities. IEEE Access, 5, 8869–8879, 2017.
10. Sahoo, P.K., Mohapatra, S.K., Wu, S.-L., Analyzing healthcare big data with prediction for future health condition. IEEE Access, 4, 9786–9799, 2016.
11. Schmidt, S.C.E., Tittlbach, S., Bös, K., Woll, A., Different Types of Physical Activity and Fitness and Health in Adults: An 18-Year Longitudinal Study. BioMed Res. Int., 2017, 2017.
12. Tayeb, S., Pirouz, M., Sun, J., Hall, K., Chang, A., Li, J., Latifi, S., Toward Predicting Medical Conditions Using kNearest Neighbors. IEEE International Conference on Big Data, pp. 3897–3903, 2017.
13. Mitchell, T.M., Machine Learning, McGraw Hill International Edition, New Yak City, 1997.
14. Du, Y., Gebremedhin, A.H., Taylor, M.E., Analysis of university fitness center data uncovers interesting patterns, enables prediction. IEEE Trans. Knowl. Data Eng., 31, 8, 1478–1490, 2019.
*Corresponding author: [email protected]
3
Study of Neuromarketing With EEG Signals and Machine Learning Techniques
S. Pal1, P. Das1, R. Sahu2 and S.R. Dash3*
1Infogain India Pvt. Ltd., Bengaluru, India
2School of Computer Science & Engineering, KIIT University, Bhubaneswar, Odisha, India
3School of Computer Applications, KIIT University, Bhubaneswar, Odisha, India
Abstract
Neuromarketing is the most rising yet undelved technique even though it has shown immense potential. It has many uses and benefits in the commercial sector as supposedly it can tell which product has potential while analyzing your competition and also stop from manufacturing products which might fail in upcoming market trends. It is supposed to fill the gap between survey results and the actual behavior of the customer at the shop.
It has not been researched well in the past due to limitations of cost-effectiveness of an EEG device. But with the promise of cheap, portable and reliable devices like Emotiv Epoc sensors and Neurosky Mindwaves, we are now able to