A computer implemented method for providing a classification model to classify a menopausal state, the method being performed by an evaluation unit of a system, the evaluation unit comprising a data storage and implementing a classification model and receiving raw sensor data via a long-range communication link, the method comprising the steps: a) determining a set of measurable body conditions from a group of body conditions of a human body, the body conditions being indicative of a menopausal state of a human body, b) determining a set of training objects, the training objects being humans being capable of adopting a menopausal state and having a known state concerning their menopausal state as menopausal state information, c) measuring, by sensors that the training objects are provided with, the measurable body conditions of the set of measurable body conditions of each of the training objects for a predetermined amount of time to provide measured body condition information for each of the training objects, and detecting occurrence of a symptom of a menopausal state, d) preprocessing the measured body condition information to provide preprocessed body condition information, by applying a low pass filter, reducing a sampling rate, applying a sliding left window or labelling perimenopausal symptoms as classification information, e) providing the computer implemented classification model adapted to classify a menopausal state, i.e. a trained classification model to classify a perimenopausal state or the absence thereof, f) inputting the preprocessed body condition information of a training object as training input information to the classification model, g) inputting the detected occurrence of the symptom of a menopausal state as training classification information to the classification model, h) adapting the classification model according to the training input information and training classification information. a) determining a set of measurable body conditions from a group of body conditions of a human body, the body conditions being indicative of a menopausal state of a human body, b) determining a set of training objects, the training objects being humans being capable of adopting a menopausal state and having a known state concerning their menopausal state as menopausal state information, c) measuring, by sensors that the training objects are provided with, the measurable body conditions of the set of measurable body conditions of each of the training objects for a predetermined amount of time to provide measured body condition information for each of the training objects, and detecting occurrence of a symptom of a menopausal state, d) preprocessing the measured body condition information to provide preprocessed body condition information, by applying a low pass filter, reducing a sampling rate, applying a sliding left window or labelling perimenopausal symptoms as classification information, e) providing the computer implemented classification model adapted to classify a menopausal state, i.e. a trained classification model to classify a perimenopausal state or the absence thereof, f) inputting the preprocessed body condition information of a training object as training input information to the classification model, g) inputting the detected occurrence of the symptom of a menopausal state as training classification information to the classification model, h) adapting the classification model according to the training input information and training classification information.