NutriHeart with Chatbot

Year : 2025 | Volume : 12 | Issue : 03 | Page : 22 34
    By

    Varun Dave,

  • Aryan Vashisth,

  • Akshat Pande,

  • V.M. Gayathri,

  1. Student, Department of Networking and Communications, Sri Ramaswamy Memorial Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
  2. Student, Department of Networking and Communications, Sri Ramaswamy Memorial Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
  3. Student, Department of Networking and Communications, Sri Ramaswamy Memorial Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
  4. Assistant Professor, Department of Networking and Communications, Sri Ramaswamy Memorial Institute of Science and Technology, KattankulathurDepartment of Networking and Communications, Sri Ramaswamy Memorial Institute of Science and Technology, Kattankulathur, Tamil Nadu, India

Abstract

Heart disease stands as one of the world’s principal reasons for human deaths since it causes major preventable fatalities each year. Healthcare institutions currently explore machine learning (ML) integration for establishing new approaches toward predicting, and acting ahead of healthcare developments. NutriHeart presents an AI-based platform that accomplishes cardiovascular risk detection early and extends heart wellness by delivering customized nutritional and lifestyle recommendations. Using Support Vector Machines (SVM) along with K-Nearest Neighbors (KNN) and Random Forest and Logistic Regression enables the platform to perform structured health parameter analysis for heart disease prediction. The main distinction between NutriHeart and conventional prediction systems includes how it integrates a user-friendly chatbot interface which delivers customized health-related information. Users receive customized nutrition planning through the chatbot after receiving their risk prediction which includes automated health guidance and tracking functions through follow-up prompts of individual action steps. The research explores how NutriHeart operates through its structural foundation and programming methods and chatbot artificial intelligence functionality. This research describes how predictive analytics should work alongside real-time user engagement to enhance both health outcomes and patient activation in cardiovascular care.

Keywords: Heart disease prediction, natural language processing (NLP), machine learning, K-Nearest Neighbors (KNN), support vector machine (SVM), random forest, logistic regression, predictive modeling

[This article belongs to Recent Trends in Parallel Computing ]

How to cite this article:
Varun Dave, Aryan Vashisth, Akshat Pande, V.M. Gayathri. NutriHeart with Chatbot. Recent Trends in Parallel Computing. 2025; 12(03):22-34.
How to cite this URL:
Varun Dave, Aryan Vashisth, Akshat Pande, V.M. Gayathri. NutriHeart with Chatbot. Recent Trends in Parallel Computing. 2025; 12(03):22-34. Available from: https://journals.stmjournals.com/rtpc/article=2025/view=232641


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Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 19/06/2025
Accepted 15/07/2025
Published 30/10/2025
Publication Time 133 Days
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