DIAGNOSIS OF DIABETES MELLITUS USING STATISTICAL METHODS AND MACHINE LEARNING ALGORITHMS


PEKEL E., ÖZCAN T.

SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES-SIGMA MUHENDISLIK VE FEN BILIMLERI DERGISI, cilt.36, sa.4, ss.1263-1280, 2018 (ESCI İndekslerine Giren Dergi) identifier

  • Cilt numarası: 36 Konu: 4
  • Basım Tarihi: 2018
  • Dergi Adı: SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES-SIGMA MUHENDISLIK VE FEN BILIMLERI DERGISI
  • Sayfa Sayıları: ss.1263-1280

Özet

The early diagnosis of the diabetes condition is crucial for cure process, because an early diagnosis provides the ease of treatment for the patient and the physician. At this point, statistical methods and data mining algorithms can provide important opportunities for early diagnosis of diabetes mellitus. In the literature, many studies have been published for solution of this problem. In this study, firstly, these studies are analyzed in detail and classified according to their methodologies and solution approaches. The main aim of this paper is to provide the comprehensive and detailed review of the diagnosis of diabetes by statistical methods and machine learning algorithms. Also, this paper presents a literature review on the diagnosis diabetes up to the end of 2017. It's identified over 425 papers, highly cited 100 ones are presented in detailed. This paper provides to guide future research and knowledge accumulation and creation of classification and prediction techniques in diagnosis of diabetes. This study shows it is clear that the combination of different machine learning algorithms and optimization models can lead to more meaningful and powerful results.