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Alexander Arman Serpen of Southview High School, Ohio, coordinates advanced research studies on machine learning algorithms to diagnose chronic kidney disease

IGI Global Publishes Exciting New Research Conducted by 16-year old Alexander Serpen

By IGI Global Scientific Publishing on Dec 29, 2016
International Journal of Biomedical and Clinical Engineering IGI Global is excited to feature emerging new research coordinated by Sylvania Southview Highschool student, Alexander Arman Serpen. Alex conducted an advanced study which employed a machine learning algorithm to extract decision-making rules that can be used to diagnose chronic kidney disease.

The algorithm was employed on actual patient data to extract decision-making rules such as age, blood sugar level, and blood pressure- these symptoms can be used to diagnose chronic kidney disease. Out of 400 people, the data set contained 250 patients positive for chronic kidney disease. The C4.5 decision tree algorithm was applied to the patient data to formulate a set of diagnosis rules for chronic kidney disease. The C4.5 algorithm utilizing 3-fold cross validation achieved 98.25% prediction accuracy and thus correctly classified 393 instances and incorrectly classified 7 instances for a total patient count of 400. The extracted rule set highlighted the need to monitor serum creatinine levels in patients as the primary indicator for the presence of disease. Secondary indicators were pedal edema, hemoglobin, diabetes mellitus and specific gravity. The set of rules provides a preliminary screening tool towards conclusive diagnosis of the chronic kidney disease by nephrologists following timely referral by the primary care providers or decision-making algorithms.

Alex presented his research at several science fairs, but took it one step further to have it published by IGI Global. He was recently interviewed in The Blade, and commented, “I wanted to get it published so I could get my name out there,” he said. “I might want to continue with this [work] in the future and I wanted to be prepared for whatever I do in the future.”

Alex's research is published the article "Diagnosis Rule Extraction from Patient Data for Chronic Kidney Disease Using Machine Learning" in the International Journal of Biomedical and Clinical Engineering (IJBCE). IJBCE's mission is to propound and encourage new ideas and techniques in biomedical engineering. The main of objective of the journal is to bridge medicine and technology, covering state-of-the-art advancements and new standards. The journal serves as a forum for researchers, clinicians, medical industrialists, healthcare professionals, and academicians to exchange ideas for developing new healthcare standards, products, and so forth.
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