International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 05 | May 2024
p-ISSN: 2395-0072
www.irjet.net
AetherWell: A Smart Digital Healthcare Solution Dr M P Pushpalatha1, Sharadhi N N2, Shivam Menda3, Veekshith D4, Mithali S R5 1Professor, Dept. of Computer Science and Engineering, JSSSTU, Karnataka, India. 2Student, Dept. of Computer Science and Engineering, JSSSTU, Karnataka, India. 3Student, Dept. of Computer Science and Engineering, JSSSTU, Karnataka, India. 4Student, Dept. of Computer Science and Engineering, JSSSTU, Karnataka, India. 5Student, Dept. of Computer Science and Engineering, JSSSTU, Karnataka, India. ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The healthcare landscape is evolving rapidly, and
recommendations, with the potential to shape the future of healthcare guidance and promote a healthier society.
the demand for intelligent systems that can assist individuals in disease identification and connect them with relevant healthcare professionals is paramount. This paper introduces a cutting-edge health recommendation system that leverages deep learning collaborative filtering, specifically Alternating Least Squares (ALS) to provide individualized recommendations enhancing accuracy and personalization. The system predicts the top ten diseases based on symptoms and also recommends a specialized doctor for each predicted disease, creating a comprehensive healthcare recommendation platform. The proposed solution aims to empower patients and healthcare providers with actionable insights and recommendations related to disease prevention, management, and overall well-being.
Key Words: Deep Learning; Collaborative filtering; Health recommendation system;
Furthermore, the significance of this research extends beyond individual well-being to broader societal implications. By empowering individuals with personalized health recommendations, the potential exists to alleviate strain on healthcare systems by promoting preventative care and healthier lifestyles. This system not only addresses the immediate need for personalized guidance but also lays the groundwork for a paradigm shift in healthcare delivery. By leveraging advanced technologies such as deep learning and collaborative filtering, opens the door to more efficient and effective healthcare interventions tailored to each individual's unique needs. Ultimately, the implementation of such a system could lead to improved health outcomes on a population scale, fostering a healthier and more resilient society.
1. INTRODUCTION
2. OBJECTIVES 1) User-Specific Health Recommendation Model
In an era marked by an abundance of health data and a growing emphasis on personalized healthcare, this paper introduces an innovative health recommendation system. By blending deep learning with collaborative filtering, it aims to revolutionize how people access and utilize health-related information, ultimately striving to enhance overall wellbeing. This system represents a significant advancement in health recommendation technology, offering tailored guidance for healthier lifestyles and more informed healthcare decisions.
Developing a collaborative filtering-based recommendation model using PySpark to provide personalized health recommendations to users based on their historical health data, preferences, demographics, and behaviors. Using techniques such as matrix factorization, the implemented model takes the user's reported symptoms as input to predict the top ten likely diseases the individual might be experiencing. The objective is to facilitate early disease detection and also significantly reduce the risk of misdiagnosis, ensuring more precise and targeted healthcare interventions.
The central challenge addressed is the necessity for an efficient and personalized health recommendation system. Despite the wealth of available health data and information, individuals often find it challenging to make informed choices regarding their well-being. The task involves harnessing deep learning and collaborative filtering methodologies to construct a robust system capable of navigating extensive healthcare data, delivering highly personalized suggestions, and directing individuals towards healthier habits and betterinformed healthcare decisions. This system endeavors to tackle this challenge and contribute to the evolution of sophisticated, data-driven solutions in the field of health
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Impact Factor value: 8.226
2) Implementation of an Interactive User Interface The task involves crafting and executing an intuitive user interface (UI) employing suitable frontend technologies (NextJS), tightly integrated with the backend recommendation system constructed using PySpark. This UI should empower users to input their health-related details, preferences, and symptoms, and subsequently receive personalized health recommendations grounded on predictions generated by the collaborative filtering model.
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