Home to Hospital: Appointment System and Machine Learning

ABSTRACT The Hospital Appointment System is a web-based solution designed to streamline the process of scheduling and managing appointments between patients and healthcare providers. Traditional appointment systems often face challenges such as long wait times, miscommunication, and inefficiency in managing patient records. This system addresses these issues by allowing patients to register, view available doctors, and book appointments online based on doctor availability. Doctors can manage their schedules and view patient histories through a secure portal. The platform includes features such as real-time notifications, user authentication, appointment rescheduling, and digital record keeping, which collectively enhance patient experience and reduce administrative workload. Developed using modern web technologies and backed by a robust database, the system ensures scalability and data security. The research emphasizes the role of digital transformation in healthcare accessibility and efficiency. Experimental results demonstrate significant improvements in appointment accuracy and patient satisfaction. Future improvements may include AI-based appointment suggestions and integration with electronic health record (EHR) systems. The Skin Disease Detection System is a deep learning-based application designed to assist in the early diagnosis of common skin conditions using image processing techniques. Skin diseases often go undiagnosed due to a lack of immediate medical access or patient awareness. This system leverages convolutional neural networks (CNNs) to analyze dermatoscopic images and classify them into multiple categories such as eczema, psoriasis, melanoma, and others. The model is trained on publicly available dermatology datasets and achieves high accuracy in identifying skin abnormalities. Users can upload images through a web or mobile interface and receive instant predictions along with risk levels. The system offers a low-cost, accessible alternative for preliminary diagnosis and can be a valuable tool in teledermatology, especially in rural or underserved areas. The research outlines model architecture, training methodology, and performance evaluation metrics. Experimental results indicate the system’s potential in supporting dermatologists and improving early detection rates. Future work will involve expanding the dataset, improving classification accuracy, and incorporating expert feedback for continuous learning.