## 1 Introduction  
Nuclear reaction cross-section data are very important to the field of medical radiobiology in both diagnostic imaging and targeted therapy for cancer treatment because they can be used to optimize established and new nuclear reaction routes, which is crucial for the optimization of the production of radionuclide [1]. One of the most common radionuclides in diagnostic nuclear medicine is Indium-111 (\({}^{111}\)In), whose relatively short decay life and low energy gamma photon emissions make it popular for radiolabeling target cells [2]. The usual production of \({}^{111}\)In is performed by irradiating cadmium with protons, which can be achieved using a particle accelerator. In addition, nuclear cross-section data are important in nuclear reactions to identify possible production pathways for maximizing the yield of radionuclide and minimizing the impurity. For example, [PERSON] et al. (2021) studied the production pathway of copper-67 via proton-induced nuclear reaction with a zinc-68 target since there is a limited supply of copper-67, and the production nuclear reaction \({}^{68}\)Zn (p, 2p) \({}^{67}\)Cu could be achieved by using incident proton energy-operable in-house medical cyclotron. Another example is that \({}^{72}\)As can also be used in theranostic therapy, specifically in positron emission tomography (PET). In the production of \({}^{72}\)As, Ge and Se targets are used in proton-induced nuclear reaction. Other than proton-induced nuclear reaction, muon capture has also previously been used in the production of various useful radionuclides such as technetium-99m, which is the decay product of molybdenum-99 [3]. Resonant photonuclear isotope transmutation has also been reported previously to use photonuclear reaction in the production of technetium-99 [4].  
Machine learning has become increasingly popular in recent years, especially for making accurate predictions by training on a large dataset. The availability of big and public curated databases in various fields of science makes a data-driven approach such as machine learning favorable in modeling physical sciences. In the field of nuclear physics, databases of evaluated and experimental nuclear cross-sections such as the Evaluated Nuclear Data File (ENDF/B-VII.0) database, the Recommended Nuclear Data for the Production of Selected Therapeutic Radionuclides database and the Experimental Nuclear Reaction Data (EXFOR) database are constantly updated with excitation functions of nuclear reactions of various important radionuclides in medical radiobiology [5; 6; 7]. Despite being a new approach in nuclear physics, machine learning has been applied to improve the accuracy of nuclear cross-section data by using random forest algorithms [8]. A random forest algorithm is used as a benchmark for the quality of the Evaluated Nuclear Data Files (ENDF) library by tracing discrepancy between simulated and experimental effective neutron multiplication factors, \(k_{eff}\). Gaussian process regression has also been performed to generate a data-driven nuclear cross-section of a nickel target using the EXFOR library as the predictor [9] and to make predictions on the uncertainty of the nuclear cross-section data.  
In this paper, a random forest algorithm is used to build models to generate the nuclear cross-section data of proton- and deuteron-induced nuclear reactions with \({}^{111}\)In as the emission. \({}^{109}\)Ag (\(\alpha\), 2n) \({}^{111}\)In, \({}^{111}\)Cd (p, n) \({}^{111}\)In and \({}^{112}\)Cd (p, 2n) \({}^{111}\)In are the reaction channels that we were interested in. The generated nuclear cross-section data were then compared with the evaluated nuclear data library TENDL-2019 and the Recommended Nuclear Data for the Production of Selected Therapeutic Radionuclides database [7; 8].