Personalized Healthcare: A Comprehensive Approach for Symptom Diagnosis and Hospital Recommendations Using AI and Location Services

Main Article Content

Seng-Keong Tan
Siew-Chin Chong
Kuok-Kwee Wee
Lee-Ying Chong

Abstract

Utilizing digital advancements, an integrated Flask-based platform has been engineered to centralize personal health records and facilitate informed healthcare decisions. The platform utilizes a Random Forest model-based symptom checker and an OpenAI API-powered chatbot for preliminary disease diagnosis and integrates Google Maps API to recommend proximal hospitals based on user location. Additionally, it contains a comprehensive user profile encompassing general information, medical history, and allergies. The system includes a medicine reminder feature for medication adherence. This innovative amalgamation of technology and healthcare fosters a user-centric approach to personal health management.

Article Details

Section
Articles

References

R. Kannan, I. S. Rosdi, K. Ramakrishnan, H. R. Abdul Rasid, M. H. L. Mohamed Rafy, S. Yusuf & S. N. A. Mohd Salamun, “Leveraging Business Data Analytics and Machine Learning Techniques for Competitive Advantage: Case Study Evidence from Small Businesses”, International Journal of Management, Finance and Accounting, vol. 2, no. 1, pp. 73–87, 2021.

L. Kaboutari-Zadeh, A. Azizi, A. Ghorbani & A. Azizi, “Designing and evaluating a mobile personal health record application for kidney transplant patients”, Informatics in Medicine Unlocked, vol. 30, 2022.

C. N. Villavicencio, J. J. Macrohon, X. A. Inbaraj, J. H. Jeng & J. G. Hsieh, “Development of a Machine Learning Based Web Application for Early Diagnosis of COVID-19 Based on Symptoms”, Diagnostics, vol. 12, no. 4, pp. 1 – 30, 2022.

P. L. T. Irawan, C. B. S. Hartanto & O. H. Kelana, “Medicine Consumption Reminder and Monitoring Application for Patients with Leprosy Disease”, Journal of Community Practice and Social Welfare, vol. 2, no. 1, 2022.

R. Wahdiniwaty, E. B. Setiawan, F. Auliardi & D. A. Wahab, “Application Model for Travel Recommendations Based on Android”, IJNMT (International Journal of New Media Technology), vol. 6, no. 1, 2019.

WebMD, WebMD Symptom Checker, WebMD LLC, https://symptoms.webmd.com/ (accessed April 4, 2023)

Symptoma, Welcome to Symptoma. Symptoma, GmbH, https://www.symptoma.com/ (accessed January 11, 2023)

OpenEMR. Fully Working OpenEMR 7.0.1 Demo, OpenEMR Foundation, Inc., https://www.open-emr.org/demo/ (accessed May 3, 2023)

Medisafe, Medisafe Pill & Med Reminder. MedisafeApp, https://play.google.com/store/apps/details?id=com.medisafe.android.client&hl=en&gl=US&pli=1 (accessed January 15, 2023)

Getdoc, GetDoc - Search and Appointmen. Jireh Group, https://play.google.com/store/apps/details?id=com.jireh.goseedoc&hl=en&gl=US (accessed February 5, 2023)

J. de V. Mohino, J. B. Higuera, J. R. B. Higuera, & J. A. S. Montalvo, “The application of a new secure software development life cycle (S-SDLC) with agile methodologies”, Electronics (Switzerland), vol. 8, no. 11, pp. 1 – 28, 2019.

A. A. Akimov, D. R. Valitov & A. I. Kubryak, “Data Preprocessing for Machine Learning”, Scientific Review. Technical Sciences, no. 2, 2022.

R. Meenal, P. A. Michael, D. Pamela & E. Rajasekaran, “Weather prediction using random forest machine learning model”, Indonesian Journal of Electrical Engineering and Computer Science, vol. 22, no. 2, pp1208-1215, 2021.

Kaggle. Disease Prediction Using Machine Learning. Kaggle Inc., https://www.kaggle.com/datasets/kaushil268/disease-prediction-using-machine-learning (accessed December 15, 2022)

S.T. Lim, J.Y. Yuan, K.W. Khaw & X. Chew, “Predicting Travel Insurance Purchases in an Insurance Firm through Machine Learning Methods after COVID-19”, Journal of Informatics and Web Engineering, vol. 2, no. 2, pp. 43 – 58, 2023.