Table 8 Input Variables and artificial intelligence that are applied the most in the identification of risk groups of osteoporosis or fractures
From: Artificial intelligence on the identification of risk groups for osteoporosis, a general review
Input variables | Artificial intelligence |
|---|---|
Abortions or stillbirths Stroke Height Arthritis Physical activities Hearing Cancer Cataract Alcohol consumption Coffee consumption Corticoids Waist circumference Diabetes Difficulty of mobility Heart condition Hepatic disease Chronic Respiratory Disease A Pain when walking Headache or migraine Duration of breastfeeding Education Estrogen therapy Hand hold medium Strength Fracture Smoking Pregnancy Hipertension Hyperlipidemia History of falling accidents Age BMI—body mass index Urinary incontinence Calcium intake Glucosamine intake Intake of milk Vitamin intake Parkinson’s disease Menopause DMO total value Number of children born Occupation Weight MMSE Score Race Gender Hormone replacement therapy Use of analgesic Use of antidiabetics | ANFIS—adpative neuro-fuzzy inference system ANN—artificial neural network CNN—condensed nearest neighbor GA—genetic algorithm LVQ—learning vector quantization MFNN—multilayer feedforward neural networks ML—machine learning NN—nearest neighbor PNN—probabilistic neural network RBF—radial basis function SVM—support vector machine LR—logistic regression |