Patients undergoing cancer chemotherapy are often at high risk of unplanned hospital admissions, because of their disease or treatment. These admissions are often to a specialist unit with limited capacity, so it is of value in planning resource allocation to know when peaks and troughs in demand are likely to occur. In this study we have sought to produce a machine-learning based model able to assess patients starting chemotherapy and their risk of subsequent unplanned admission, with a view to producing a forecast of likely unplanned admission numbers in subsequent weeks. Our models performs as well as or better than previously reported models, with an AUROC of 0.82 for the best models, but when applied to the expected number of admissions in each week, there remains a substantial amount of unexplained variability in observed admission numbers beyond our predicted values.