Among diabetic disorders, diabetes mellitus is the most common ailment. This will reduce human effort, time, cost, unnecessary medication and side effects.ĭiabetes is a metabolic disorder that affects a large amount of people globally. The proposed system classifies diseases and their severity and suggests medications without any conflict. The proposed system coordinates these issues through decision support system and reduces unnecessary medications and side effects. The patient who suffer from more than one ailment may take multiple and repetitive pills which results in adverse side effects. The Linear classifier SVM and parallel ensemble Random Forest Algorithm are applied appropriately to predict the disease. After doing careful analysis this chapter aims to suggest better algorithm for which considers people above 50 years of age who need continuous medical support due to the increase of chronic diseases and insufficient medical assistance. This automatically increases chronic diseases and aging related ailments in the society. Life span of people has increased with technological support and statistics reveals that by 2030 the population of elderly people (above 65 years) will become double. The phenomenal growth of computing and connectivity has redefined the Healthcare environment with the help of Information and Communication Technologies (ICT). On the other hand, the Healthcare industry is combating against workforce crisis due to inadequate infrastructure, shortage of doctors, specialists, and medical assistants. There has been increasing demand for chronic care in rural area. Healthcare is an essential service as a well as high revenue yielding sector. Finally, after filtering, Recommender suggests the books to the users based on their interests accurately which satisfies the customer with an average accuracy of 85%. In additional, PI technique is also used to retrieve data from popular books by ranking in the database. CBF system and CF system are used to filter reviews. Now, grouping of reviewers is done dependent on the kind of review they are given and their personal details such as age, region and sex by utilizing MySQL. Firstly, the input data which is document level reviews transferred from analysis stage to analyzes the sentiment of the user and removes noisy data. It recommends books to the registered users in the system and also to the new users. A novel solution for recommending accurate books to the user was proposed by applying an integration of the Collaborative Filtering (CF) and Content-Based Filtering (CBF) with Popularity Index (PI) methods. In recent times, Recommendation System (RS) performs a main position in assisting clients to discover the best books of their interest.
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