This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset .
Read moreWhat is diabetes pedigree?
DiabetesPedigreeFunction: Diabetes pedigree function (a function which scores likelihood of diabetes based on family history ) Age: Age (years) Outcome: Class variable (0 if non-diabetic, 1 if diabetic)
Read moreWhat is the percentage of diabetes in the Pima TE Data?
The incidence and prevalence of diabetes mellitus were determined in 3733 Pima Indians aged 5 years or over by periodic examinations over a 10-year period. The examinations included modified glucose tolerance tests and medical record review. The age-sex adjusted prevalence rate was 21.1% (SE = 0.7%).
Read moreHow skin thickness is related to diabetes?
Skin thickness (epidermal surface to dermal fat inter- face), which is primarily determined by collagen con- tent, is greater in insulin-dependent diabetes mellitus (IDDM) patients who have been diabetic for >10 yr (11,12). This possibly reflects increased collagen cross- linkage and reduced collagen turnover (2,3).
Read moreWhy is diabetes so important?
Increases the all-cause mortality rate 1.8 times compared to persons without diagnosed diabetes . Increases the risk of heart attack by 1.8 times. Is the leading cause of kidney failure, lower limb amputations, and adult-onset blindness.
Read moreCan skin thickness be zero?
Skin Fold Thickness: For normal people, skin fold thickness can’t be less than 10 mm better yet zero . Total count where value is 0: 227. BMI: Should not be 0 or close to zero unless the person is really underweight which could be life-threatening.
Read moreWhich classification algorithm performs better for diabetes dataset?
They found for better accuracy, Adaboost can be applied to predict diseases like diabetes, coronary heart disease, and hypertension. Sisodia et al. [18] found that, among the applied machine learning methods SVM, NB, and DT on PIDD, the NB classifier shows better accuracy at 76.30%.
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