An Effective Diabetes Prediction System Using Machine Learning Algorithms
Devansh Trivedi, Aditi Amit Malkar, Altmash Siddique, Rishank Shah
Diabetes Prediction, Machine Learning, Manual Information, Random Forest, Decision Trees, Adaptive Boosting
Diabetes is a prevalent metabolic disorder that affects a significant number of people globally. Timely detection and treatment of diabetes can prevent complications and improve health outcomes. The healthcare industry is facing an increasing demand for better patient care and disease prediction systems. This study proposes a Disease Prediction System that integrates various features, including an AI ChatBot, Diabetes Prediction System, Chat and Appointment Booking System, to improve disease prediction accuracy. The Random Forest algorithm is utilized in the Diabetes Prediction System, which enhances the overall accuracy of the system. With multiple inputs, the system becomes proficient in accurately classifying diseases and predicting outputs. The system's accuracy was evaluated using a patient information dataset, resulting in an overall accuracy of 90.4%. These results demonstrate the Disease Prediction System's potential to improve healthcare outcomes by providing timely and accurate disease prediction. In conclusion, this study's proposed system has the potential to significantly benefit healthcare providers and the medical field. With its high disease prediction accuracy, efficient disease classification, and user-friendly features, this system can assist healthcare professionals in making precise diagnoses, providing effective treatments, and enhancing patient outcomes.
Article Details
Unique Paper ID: 158893

Publication Volume & Issue: Volume 9, Issue 10

Page(s): 969 - 973
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