AN Advanced Medical Tourism System in India Using Deep Learning Algorithms

Traveling abroad for medical treatment is a practice known as “medical tourism,” it has become increasingly popular in recent years. Healthcare professionals, legislators, and patients must know the top medical tourism destinations. This study suggests using an advanced learning algorithm-based technique to forecast and analyze the trends in medical tourism destinations. The proposed approach is the combination of Deep Neural Networks (DNN) and Multilayer Perceptron (MLP) to analyze large datasets, including patient testimonials, healthcare infrastructure, medical service quality, economic indicators, and geopolitical stability. The objective is to create a thorough model that can accurately pinpoint the newly well-liked locations for medical travel. The model is trained on historical data using the suggested advanced learning algorithm, which makes it capable of identifying trends, correlations, and changing medical tourism preferences. With its ability to adjust to the ever-changing healthcare environment and patient preferences, the model offers up-to-date information on the most popular travel destinations. By giving healthcare stakeholders a data-driven tool for predicting trends in patient preferences and healthcare locations, the study seeks to advance medical tourism research. The algorithm can also help legislators optimize healthcare laws and practices to attract medical tourists. The research findings can improve the competitiveness and adaptability of healthcare providers by enabling them to position themselves strategically in the global medical tourism market. Stakeholders can accommodate the changing demands of people seeking healthcare treatments overseas by making educated decisions and knowing the variables driving the popularity of medical tourism destinations.