Machine Learning for Healthcare Applications. Группа авторов

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Machine Learning for Healthcare Applications - Группа авторов

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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.

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
Bar chart depicts recall: Model-I vs Model-II.

      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]

      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

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