Özerk YAVUZ

A Classification and Clustering Approach Using Data Mining Techniques in Analysing Gastrointestinal Tract

In having a healthy life, right nutrition with a life style balanced with exercise constitutes and important value. This is mainly important for bodily functions that are provided by organs and systems of the body which is also valid for our digestion and excretion systems. In having a healthy life right nutrition with a life style balanced with exercise constitutes and important value. Digestion is one of the main functions of human metabolism that starts in mouth with mechanical and chemical reactions and ends with the expellsion of digestive residues from the anus, following the large intestine. Many hormones and enzymes have influence in the digestion of carbonhydrates, proteins and fats in the metabolism. With the key and luck structures that enzymes have and with the hormonal homeostasis that the metabolism have processing of different monomer and polimer structures are being handles. After the absorbation of digested food contents in duodenum and small intestine. Digestive residues are expelled from the anus, following the large intestine. If there is a malfunction in some part of the digestive system or characteristics of the food taken some digestive and excretion problems may occur. Some of these most known problems are constipation and hemorrhoids. In this phenemonelogical, single case study, some of the factors that have influence in defecation with development of constipation and hemorrhoids investigated with the aim of enhancing exploratory and confirmatory understanding for medical specialists and society at large.

Anahtar Kelimeler:

Hemmoroids, Constipation, Eating Habits, Healthy Lifestyle, Clustering, Classification, Data Mining, Machine Learning, Quantitative Analysis, Supervised Learning, Unsupervised Learning

A Classification and Clustering Approach Using Data Mining Techniques in Analysing Gastrointestinal Tract

Keywords:

Hemmoroids, Constipation, Eating Habits, Healthy Lifestyle, Clustering, Classification, Data Mining, Machine Learning, Quantitative Analysis, Supervised Learning, Unsupervised Learning,

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