Health Insurance Recommendation using Probabilistic Data Structures

Recommender systems are constantly evolving in the health insurance industry, with a focus on speed and efficiency. To meet the demands of clients, insurance providers need to generate personalized policy recommendations that are quick and accurate. This paper suggests a health insurance recommendation system that combines advanced algorithms to offer efficient and personalized recommendations based on user preferences and similarities. The proposed system leverages the MinHash and LSHForest algorithms, as well as collaborative filtering, to improve the recommendation process. MinHash approximates the Jaccard similarity between policies, while LSHForest enables faster search for similar policies. Collaborative filtering considers user preferences and similarities among policies and users. To ensure user input is secure and legitimate, the system employs a Bloom filter. This filters out unauthorized access to sensitive healthcare data and validates user input. The architecture of the system addresses various challenges related to personalization and privacy in health insurance recommendation systems. Based on the results, the proposed system surpasses existing approaches in accuracy, efficiency, and privacy preservation. This has the potential to be advantageous for both insurance providers and users as it helps streamline the process of choosing appropriate health insurance plans that align with individual requirements and preferences.

I. Introduction

Healthcare is one of the crucial factors in human life. As healthcare costs continue to rise, ensuring people have access to better health insurance plans becomes increasingly important. Health insurance companies strive to offer policy recommendations tailored to individual needs and preferences. In this concern, there is a need for an efficient and personalized health insurance recommendation system. This research paper proposes a health insurance recommendation system that utilizes MinHash and LSHForest algorithms with collaborative filtering to offer users efficient and personalized insurance policy recommendations. The proposed system employs a Bloom filter to ensure the legitimacy and security of user input. Healthcare plays a vital role in ensuring human survival and overall well-being. It forms the cornerstone of economic and social progress, as individuals in good health are more productive and actively participate in societal growth. [1] Affordable and quality healthcare is crucial for people to lead healthy and fulfilling lives. Health insurance is essential to healthcare, as it helps mitigate the financial risks associated with medical emergencies and expenses. Therefore, there is a need for an efficient and personalized health insurance recommendation system that can help people to select the most suitable health insurance plan based on their requirements and preferences. The system utilizes MinHash and LSHForest algorithms with collaborative filtering to offer users efficient and personalized insurance policy recommendations. The system employs a Bloom filter to ensure the legitimacy and security of user input. The system’s design aims to tackle the obstacles related to personalization and privacy in health insurance recommendation systems. [2] MinHash Algorithm: The MinHash algorithm approximates the Jaccard similarity between policies. The algorithm generates a signature for each policy by randomly permuting its attributes and selecting the smallest hash value. The Jaccard similarity of their signatures approximates the similarity between policies. This approach enables fast and efficient similarity search and reduces the computational complexity of the system. The LSHForest algorithm is utilized to provide faster search results. The algorithm employs a forest of LSH tables to partition the space of policy signatures. Each table is constructed using a different set of hash functions, and policies with similar signatures are mapped to the same bucket. The LSHForest algorithm enables efficient search and retrieval of policies similar to the user’s preferences. [4] By incorporating collaborative filtering, the accuracy of policy recommendations is further improved, taking into account user preferences. The algorithm utilizes user-item interactions to predict the policies that align with the user’s preferences. [3] The system uses the collaborative filtering approach to generate personalized recommendations for each user based on their historical interactions with policies. [5] The proposed system employs a Bloom filter to ensure legitimate and secure user input. The Bloom filter is a probabilistic data structure utilized to determine the membership of an element in a set. The system uses the Bloom filter to validate user input and prevent unauthorized access to sensitive healthcare data.