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.
Keywords
Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Medical Diagnosis, Healthcare System, Disease Prediction, Clinical Decision Support System (CDSS), Healthcare Automation, Neural Networks, Predictive Analytics, Patient Data, Electronic Health Records (EHR), Di- agnostic Accuracy, Smart Healthcare, Data-Driven Medicine
Citations
IRE Journals:
Anuradha Tiwari, Sameer Awasthi "AI Powered Medical Diagnosis System" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 504-511 https://doi.org/10.64388/IREV9I6-1712560
IEEE:
Anuradha Tiwari, Sameer Awasthi
"AI Powered Medical Diagnosis System" Iconic Research And Engineering Journals, 9(6) https://doi.org/10.64388/IREV9I6-1712560
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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