I have a dataset with some gait parameters (step length, stride length etc) of 100 people taken 3 times at different time(in every 6 months). Now I have to train my model on this dataset and predict if the person has a disease or not for any new person data that is given. How can I take all this 3 data of parameters in my model for training considering time factor. I checked time series forecasting but it looks like for that the dataset should be dependent on continuous time instances.
You can process the data with dynamic time warping (DTW). DTW has shown to be effective in mapping different speeds of gait to the same time grid. Once the data is mapped to the same time grid, standard time series analysis can be applied.