Skripsi

PREDIKSI POTENSI PENGIDAP PENYAKIT DIABETES MELLITUS BERDASARKAN FAKTOR RESIKO DENGAN METODE ASSOCIATION RULE

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Penilaian

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The discovery of knowledge from medical databases is important in order to make effective medical diagnosis. The aim of data mining is extract the information from database and generate clear and understandable description of patterns. In this study we have introduced a new approach to generate association rules on numeric data. We propose a modified equal width binning interval approach to discretizing continuous valued attributes. The approximate width of the desired intervals is chosen based on the opinion of medical expert and is provided as an input parameter to the model. First we have converted numeric attributes into categorical form based on above techniques. We discover that the often neglected pre-processing steps in knowledge discovery are the most critical elements in determining the success of a data mining application. Lastly we have generated the association rules which are useful to identify general associations in the data, to understand the relationship between the measured fields whether the patient goes on to develop diabetes or not. We are presented step-by-step approach to help the health doctors to explore their data and to understand the discovered rules better.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2007000907	T40709	T407092020	Central Library (REFERENSI)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T407092020

Publisher

Inderalaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer., 2020

Collation

viii, 59 hlm.; ilus., tab.: 28 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem informasi
Ilmu Komputer
Metode Association Rule

Specific Detail Info

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Statement of Responsibility

EM

Other version/related

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