A Remote Diagnostic System Using Machine Learning for General Disease Detection

The use of advanced technologies such as machine learning in healthcare shows great potential in improving diagnosis and patient care. This article presents a new method for disease prediction and planning studies designed to aid early detection of disease and facilitate access to treatment. The system accurately predicts disease by analyzing medical data, such as patient history, symptoms, and diagnostic data, using machine learning algorithms. The system achieves disease prediction findings by identifying patterns and relationships in the data using various classification techniques such as Gaussian Naïve Bayes and Random Forest. The proposed system also integrates user relationships from patients to make disease predictions. Symptoms and related information can be entered and instant predictions of potential diseases can be obtained. Additionally, the system allows making appointments with doctors based on symptoms and providing timely treatment and treatment. Key features of the system include flexibility, adaptability to different clinical environments, and the ability to continuously learn from new information, thus improving prediction accuracy over time. In addition, stringent measures have been taken to ensure data confidentiality and security and to comply with health management standards. Through empirical evaluation and research data, the effectiveness and reliability of the proposed method demonstrate its ability to improve health by enabling disease management to improve the doctor-patient relationship. The results of this research have significantly contributed to the advancement of smart medicine, paving the way for improved medical outcomes and utilization of hospital resources.