## 1 Introduction  
Communication disorders include disorders of hearing, voice, speech, and language [1]. Their prevalence was estimated as 5 to 10% according to self-report in surveys. Data from audiometric testing suggested even much higher prevalence, e.g., up to 65% in elderly Canadians [2]. Delayed or suboptimal treatment decisions may result in avoidable complications and follow-up costs. In particular, they can result in reduced quality of life, reduced career opportunities, early retirement, depression, or social isolation. Three particular types of hearing loss are cochlear gain loss (CGL), loss of inner hair cells (IHCL), and cochlear synaptopathy (CS). CGL is caused by damage of outer hair cells (OHCs), IHCL refers to the damage of IHCs, and CS is a damage of the synapses between the IHCs and the auditory nerve (AN) [3, 4, 5, 6, 7, 8, 9]. Hearing aids and cochlear implants (CIs) are common approaches for treating impaired hearing and deafness. While most implanted patients benefit from cochlear implantation, hearing may be worse after implantation than before for a one-digit percentage of them [10]. Clinicians aim at optimizing clinical outcome via selecting a hearing aid, a CI, or combinations of the two [11]. The decision for implanting a CI is crucial because it irreversibly damages hair cells. Moreover, the different types of impairment (e.g., CGL, IHCL, and CS) are neither mutually exclusive, nor binary, which further complicates the decision. Furthermore, non-cochlear impairment,e.g., conductive hearing loss or impairment of the AN, influences the decision but is beyond the scope of this paper. The objective of this work was to conduct a computational simulation study on the indication of cochlear implantation. AN firing rates of hearing-impaired individuals were predicted and compared for (i) unaided hearing, (ii) acoustical amplification, (iii) electrical stimulation, (iv) electrical stimulation combined with acoustical amplification, and (v) a normal hearing (NH) reference. For automatic fitting of the prostheses, parameters of options (ii) to (iv) were optimized to minimize the difference between predicted firing rates and the normal hearing (NH) reference. Acoustic amplification was optimized by finding optimal low-level gains of a multi-channel compressor. Electrical stimulation was optimized by finding the optimal electrode insertion depth. The parameters of hearing loss were the following: CGL ranged from 0 to 35 dB, and IHCL from 0 to 60 dB, while a frequency-flat and a frequency-sloping component were distinguished for each of them. For simulating CS, nerve fibers with low, middle, and high spontaneous rates were distinguished. As a first objective, the one of options (i) to (iv) that supposedly worked best for a particular individual was identified with respect to the parameters of hearing impairment. The one option that resulted in AN firing rates that were most similar to the NH reference was presumed to be the best. Similarity was assessed by a 'loss of action potentials' (LAP) score, which was a root mean square difference of AN firing rates adjusted for the 'costs' of the used hearing prosthesis options. In addition, examples of personalized bar charts of LAP scores ranked options (i) to (iv). These charts are supposed to reflect a clinical recommendation based on the cost-adjusted AN firing rate difference. As a second objective, an implantation recommendation was obtained that reflected whether the best option involved a CI. In addition, the influence of the cost adjustment parameters was investigated in detail. Finally, ROC curves were used for predicting implantation recommendations using single hearing loss parameters, whereas logistic regression and a support vector machine used multiple predictors. The remainder of the article is structured as follows: In the beginning of Section 2, an overview is given over the used computational models and their combination. In Section 2.1, the models of the AN firing rates are explained.