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A Study on Chronic Kidney Disease Prediction Using Supervised Learning Methods and Data Cleaning Skills
|關鍵字:||監督式學習;資料重整技術;慢性腎臟病;Supervised Learning Methods;Data Cleaning Skills;Chronic Kidney Disease||摘要:||
台灣腎臟醫學會發行的2017年台灣腎病年報內容提到，台灣末期腎臟病（End Stage Renal Disease，ESRD）的發生率仍然為世界第一，遠高於歐、美洲和日本等國家；洗腎的盛行率及發生率，亦是世界第一。以衛生福利部中央健康保險署醫療費用統計，急、慢性腎臟病花費四百六十億元，比例佔全民健保總支出的7.31％；而全國洗腎總人口數則是增加至八萬五千人，也都創下歷史的新高記錄。在現今台灣社會開始正式歩入「高齡化社會」的環境下，慢性腎臟病對國家及人民的影響是值得嚴肅去正視的重要議題。
In 2017 Taiwan Nephrology Annual Report issued by the Taiwan Society of Nephrology, the incidence of end stage Renal Disease(ESRD) in Taiwan is still the highest in the world, much higher than in countries such as Europe, America and Japan. Prevalence and incidence are also the highest in the world. According to the medical expenses of the Central Health Insurance Department of the Ministry of Health and Welfare, acute and chronic kidney diseases cost 460 billion NT dollars, accounting for 7.31% of the total health insurance expenditure. The total number of dialysis patients nationwide has increased to 85 thousand, which is also a record high in history. In today's Taiwanese society, which has officially entered the 'Aging society ', the impact of chronic kidney disease on the country and the people is an important issue that deserves serious consideration.
This thesis studies the data cleaning skills to simulate the imbalance data of a large number of hospital patients, and reduces the number of feature attributes for and combines medical data to build supervised learning algorithm for predicting disease, and attempts to establish a fast and reliable model to predict Chronic Kidney Disease, the results show that the accuracy of the prediction results of the four algorithms models can be as high as 99%. In the future, there is an opportunity to predict the actual examine data, and it can really help the doctor or clinician to make correct and reliable diagnosis. early detection and early treatment, reducing chronic kidney disease patients, avoiding government health insurance budget overruns.
|Appears in Collections:||資訊管理學系|
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