Year: 2025
Type: article
Abstract: It provides immediate prediction results that would help users understand potential health risks and seek immediate medical consultations. The system increases efficiency by reducing repetitive diagnostic tests in multiple disease predictions on a single platform. Experimental results have shown that the proposed system performed with high accuracy with consistency across different datasets. The study has indicated the potential of AI in improving healthcare accessibility and providing support to medical professionals during their decision-making. The system is scalable for future expansion and integration of additional diseases. The AI-Powered Medical Diagnosis System is designed to offer early and reliable prediction of multiple life-threatening diseases by the use of techniques in machine learning. This system is targeted at the prediction of five major diseases: Heart Disease, Cancer, Diabetes, Hypothyroidism, and Parkinson’s Disease, using patient health parameters and symptom data. The key objective of this research will be to improve early diagnosis, minimize human error, and facilitate a cost-effective solution for preliminary medical assessment. Our system achieved 92% accuracy across 5 disease datasets, showing consistent performance This system accepts the patient’s data via an interface: age, blood pressure, glucose levels, thyroid hormone values, and neurological indicators. The input would be further processed for better accuracy by using data preprocessing techniques like normalization, feature scaling, and handling missing values. It trains different machine learning algorithms such as logistic regression, SVM, and random forest for efficient classification and prediction of disease probability. It provides immediate prediction results that would help users understand potential health risks and seek immediate medical consultations. The system increases efficiency by reducing repetitive diagnostic tests in multiple disease predictions on a single platform. Experimental results have shown that the proposed system performed with high accuracy with consistency across different datasets. The study has indicated the potential of AI in improving healthcare accessibility and providing support to medical professionals during their decision-making. The system is scalable for future expansion and integration of additional diseases.
Authors: Anuradha Tiwari, Sameer Awasthi
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4417233350
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