Nowadays Machine Learning algorithms are commonly used in healthcare applications in order to help physicians in diagnosis or to find possible relations between measured biomedical parameters. In this thesis, starting from the AGES-Reykjavik Database, predictive analysis is done using both regression and classification ML algorithms. The Database is composed of 11 NTRA parameters extracted from Computed Tomography (CT) scans of mid-femur section of a 65-95 years old population, (4 related to muscle tissues, 4 to the fat, and 3 to the connective tissues) and by 25 measurements of which the most relevant are Body Mass Index (BMI), Cholesterol (SCHOL) and LEF biometric parameters (normal/fast gait speed, time up-to-go, and isometric leg strength).