Is it possible to predict using fewer number of features than the number of features that was used in training the model?
Machine learning model is nothing but an equation (hypothesis), derived from the features (data set) used for model training. Using training dataset we optimize this equation by changing the coefficient(also known as weight) values with each iteration(or with each batch).

For example:

 where x1, x2, x3 are features and theta1, theta2, theta3 are weights...that we have to optimize with each iteration.


  In order to get the results from this function, we need some value for
  x1, x2,and x3.  This function wont work in case you feed just either
  of these values.
  
  Best we can do in your case is to feed some average values for rest of
  the missing features. But then wont expect much from the model.