target="_blank" rel="nofollow" href="#ulink_c897ae7b-5566-5001-81a3-8935b64439d8">Figure 2.6 shows the F1-score comparison between the two models which are proposed in this chapter and it is observed the model-II gives more accuracy than the model-I. Table 2.5 shows the F1-Score comparison between the model-1 and model-2.
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.
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*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
