Affiliations: [a] Computer Science Department, KIET, Ghaziabad | [b] Research Scholar in Noida International University, Noida | [c] Noida International University, Noida, and Computer Science Department, KIET, Ghaziabad | [d] City University, Peating Jaya, Malaysia
| [e] Department of Media and Educational Informatics, Faculty of Informatics, Eotvos Lorand University, Hungary
| [f] Faculty of engineering and Information Science, University of Wollongong in Dubai, UAE
Correspondence:
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Corresponding author. Manoj Kumar, Faculty of Engineering and Information Science, University of Wollongong in Dubai, UAE. E-mail: wss.manojkumar@gmail.com.
Abstract: In recent generations of the digital world medical data in Recommender Systems. Health Care Recommender System (HCRS) analyses the medical data and then predicts the user’s or patient’s illness. Nowadays, healthcare data is used by various users or patients in recommendation systems which are useful for everyone. Analysing and predicting medical data provides awareness to users and these data predictions may be enriched using various techniques of RS. Machine learning techniques are used to make sure that health data is reliable and of high quality. In every RS the issues are targeted such as scalability, sparsity and cold start problems. In many social networking applications, these issues are resolved using ML algorithms. However, there is a significant gap between IT systems and medical diagnosis. The fuzzy genetic method is used in HCRS in order to bridge the gap between IT and healthcare applications. Through the use of the mutation and crossover operators, a real-value genetic method is used in this to compute similarity. With the user’s extra personalized information, fuzzy rules are later generated for the database. The Hybrid fuzzy-genetic method, also known as this situation, combines both techniques to improve recommendation quality. Utilizing this method will improve the quality of the recommendation process by discovering the most precise similarity measures among different users. Six factors are subjected to fuzzification, including age, gender, employment, height, weight, and region. Genre-interesting measure weights are then used, including Very Light, Light, Average, Heavy, and Very Heavy. Finally, the evaluation metrics used MAE and RMSE to evaluate the recommendation accuracy which showed the best results in comparison with baseline approaches such as Convolutional Neural Networks and Restricted Boltzman Machine.
