Classification performance analysis in medical science: Using kidney disease data

R. A. Jeewantha, Malka N. Halgamuge, Azeem Mohammad, Gullu Ekici

Research output: Book chapter/Published conference paperConference paperpeer-review

5 Citations (Scopus)


Health-care practices face data storage problems in the growing world. Huge data storage demands have caused undeniable data storage problems leaving health practitioners exclaimed. Without delay, accumulated data becomes too difficult to analyzed and handled by traditional approaches. A solution to this problem is urgently needed. One possible answer to this problem is Data mining that delivers the technology and procedure to convert these embankments of ordinary data into meaningful evidences for futuristic planning and decision-making. Data mining is a tool that not only solves the problem of piled up data; nonetheless it similarly turns it into meaningful data themes based on reoccurrences of trends in the data. The healthcare trade is mostly an "information and document rich industry," and manual handling is not feasible in practical life. These huge volumes of data have been key to the arena of data-mining to generate associations among the attributes and extract expedient information. Recent research shows that combating Kidney diseases is a complex assignment that involves considerable knowledge and experience for annual testing and screening. In developed nations, Kidney diseases have become a silent killer, that makes key factors of disease burden in third world nations. Various data mining procedures are available for forecasting diseases such as clustering, classification, association rules, regression, and summarizations. The key objective of this study is to analyze datasets collected from 400 patients grounded on 25 different attributes attended for treatment for Chronic Kidney Disease (CKD), after using classification methods to forecast class precisely. Our analysis illustrates that Multilayer Perceptron is the most suitable classification method that outperforms the highest classification accuracy by 99.75% (0.0085 error) with only 5% of fluctuation among algorithm measures. Introspectively, the computational time, Multilayer Perception can be time-consuming comparatively, when it comes to deal with billions of data. Nonetheless, for the field of bioinformatics and medical science accuracy, the key objective is to deal with sensitive data because, a single error can lead to a disastrous confidentiality breech. Hence, our results show that Multilayer Perception classification method is the most accurate and suitable classification algorithm that could be used in the field of bioinformatics and medical science, for further data analysis and predictions. This paper will be useful for many medical institutions and work-related bioinformatics in pursuance to understand the prediction accuracies of data patterns in related work.
Original languageEnglish
Title of host publicationICBDR 2017 - Proceedings of the 2017 International Conference on Big Data Research
Place of PublicationUnited States
PublisherAssociation for Computing Machinery (ACM)
Number of pages6
VolumePart F132530
ISBN (Electronic)9781450353564
Publication statusPublished - 22 Oct 2017
Event2017 International Conference on Big Data Research, ICBDR 2017 - ANA Crowne Plaza, Osaka, Japan
Duration: 22 Oct 201724 Oct 2017 (Conference website) (Conference proceedings)


Conference2017 International Conference on Big Data Research, ICBDR 2017
Other2017 International Conference on Big Data Research aims to bring together researchers and practitioners from academia and industry to discuss latest progress and development in this fields. ICBDR 2017 would be the international platform for knowledge sharing as well as creating favorable atmosphere for collaboration initiations. This event will include contributions by renowned plenary and invited speakers, oral presentations, posters sessions and technical exhibition that relate to the topics dealt with in the Scientific Program.
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