Data-Driven Clinical Decision Support System for Medical Diagnosis and Treatment Recommendation
Shubham Rathi1, Mahesh Motwani2, Manish Ahirwar3
1Shubham Rathi, Computer Science and Engineering, University Institute of Technology, RGPV, Bhopal (M.P), India.
2Mahesh Motwani, Computer Science and Engineering, University Institute of Technology, RGPV, Bhopal (M.P), India.
3Manish Ahirwar, Computer Science and Engineering, University Institute of Technology, RGPV, Bhopal (M.P), India.
Manuscript received on 23 August 2019. | Revised Manuscript received on 05 September 2019. | Manuscript published on 30 September 2019. | PP: 3660-3668 | Volume-8 Issue-11, September 2019. | Retrieval Number: K19500981119/2019©BEIESP | DOI: 10.35940/ijitee.K1950.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presents a Data-Driven Clinical Decision Support System (CDSS) using machine learning. The proposed system predicts the possibility of diseases based on the patient’s symptoms. It suggests lab tests and medication related to the disease. Lab test results are analyzed to check the probability of liver and kidney diseases. The proposed system uses face recognition to identify the patient. Face recognition module retrieves the Patient Health Record and provides patient information and health records access to the doctor and medical staff. The system is developed using Python Django for Backend, React.JS for User Interface and PostgreSQL as the relational database. The system uses Logistic Regression for possible disease prediction, Support Vector Machine for liver disease prediction, Random Forest for chronic kidney disease prediction. The result of the proposed data-driven clinical decision support system is compared with a doctor’s disease analysis to measure the effectiveness of the proposed system. This kind of system can help doctors in providing better care and predict the disease at an early stage.
Keywords: Chronic Kidney Disease Prediction, Clinical Decision Support System, Disease Prediction, Machine Learning, Liver Disease Prediction
Scope of the Article: Biomedical Computing