Dr. M Dileep Kumar, R Kavya, K Palvasha, S Harshith Raj , A Shashank 03 09 The Patient Billing Tracker and Prediction system is designed to manage analyze and predict patient billing information in a structured and efficient manner Hospitals and healthcare organizations generate a large volume of billing records related to consultations diagnostic tests medicines treatments procedures room charges and other medical services Managing these records manually can be timeconsuming and may result in calculation errors missing information and difficulty in tracking payment status The proposed system provides a centralized platform for recording patient billing details monitoring expenses tracking paid and pending amounts and analyzing historical billing patterns The collected billing data is first preprocessed to handle missing values duplicate records inconsistent information and categorical attributes Exploratory data analysis is performed to identify relationships between patient characteristics medical services treatment types and total billing amounts Data visualization techniques can be used to display billing trends payment status departmentwise expenses and monthly revenue Machinelearning algorithms such as Linear Regression Decision Tree Random Forest and Gradient Boosting can be applied to analyze historical billing data and predict the possible billing amount for new or ongoing patients The system can also classify billing records based on payment status and identify patients with pending payments Model performance can be evaluated using suitable metrics such as Mean Absolute Error MAE Mean Squared Error MSE Root Mean Squared Error RMSE and R score for billing amount prediction The system provides useful dashboards and reports that help hospital administrators and authorized staff monitor financial information efficiently Overall the Patient Billing Tracker and Prediction system combines billing management data analysis visualization and machine learning to improve billing transparency support financial planning and provide datadriven insights into patient healthcare expenses Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026