A privacy-aware deep learning framework for health recommendation system on analysis of big data

In recent technological advancement, the health recommendation system is gaining attention among the public to acquire health care services online. Traditional health recommendations are insecure due to the lack of security constraints caused by the intruders and not suitable to suggest appropriate recommendations. Thus, it creates hesitation in the minds of the people to share sensitive medical information. Hence, it is essential to design a privacy-preserving health recommendation system that should guarantee privacy and also suggest top-N recommendation to the user based on their preferences and earlier feedback. To cope with these issues, we propose a stacked discriminative de-noising convolution auto-encoder-decoder with a two-way recommendation scheme that provides secure and efficient health data to the end-users. In this scheme, privacy is assured to users through the modified blowfish algorithm. For structuring the big data collected from the patient, the Hadoop transform is used. Here, the two-way system analyzes and learns more effective features from the explicit and implicit information of the patient individually, and finally, all the learned features are fused to provide an efficient recommendation. The performance of the proposed system is analyzed with different statistical metrics and compared with recent approaches. From the result analysis, it is evident that the proposed system performs better than the earlier approaches.

Automated summary

With recent technological advancements, the health recommendation system has gained attention. Traditional systems are insecure and raise privacy concerns, leading to the development of a system that guarantees privacy and provides personalized recommendations. The innovative system, which incorporates various data processing methods, outperforms other approaches based on performance analysis.