## 2 Related Work  
In 2016, [PERSON], et al., have built an excel sheet as an audiogram classifier with pre-set inputs that can be defined according to inclusion criteria in the clinical trial. This tool provides inclusion decision based on the predefined audiological criteria [18]. Then, in 2018, [PERSON], et al. have classified the hearing tests data in two stages. The first stage is unsupervised learning to define trends and spot patterns in data obtained from different hearing tests. In the second stage a supervised learning algorithm is built in which different outcomes from different hearing tests were explored. In the second stage, the subjects were assigned a profile then the data were analyzed again to find the best classification structure of the subjects into the four auditory profiles. This classifier was based on data analysis to audiograms which reflects the loss of sensitivity and other hearing tests to reflect loss of clarity that was not captured by the audiogram [19]. [PERSON] et al., in 2019 have also combined unsupervised and supervised machine learning methods to map audiograms to a small number of hearing aid configurations. The target of this study was to use these configurations as a starting point for hearing aid fitting. This method was applied in two steps, the first one started by performing different unsupervised clustering algorithms to determine a limited number of pre-set configurations for a hearing aid. The centroids of the clusters were chosen to represent fittings targets which can be used as starting configurations for hearing aid adjustments for each individual. The second step was to assign to each audiogram a class based on the results from the first stage comfort target clustering. Various supervised machine learning techniques were used to assign to each audiogram a pre-set configuration. The classifier accuracy of the second stage was low when they selected single configuration and it was improved when they allowed two configurations to each audiogram [20]. In 2018, a research team developed their first steps of a machine learning classifier by the use of unsupervised learning to cluster audiograms [21]. In this work, audiograms were clustered with the target to make them maximally informative audiograms. Then the clustered data was prepared to be a good training set for supervised machine learning classifiers. They built an approach to get a set of non-redundant unannotated audiograms with minimal loss of information from very big data set. In 2020, the same group used the data preparation procedure carried out by them to produce a machine learning classifier. They applied supervised ML to 270 audiograms annotated by three experts in the field. The results have good accuracy to annotate the audiograms concisely in terms of shape, severity and symmetry [12]. The classifier can be integrated as a mobile application to help the user to describe audiogram concisely so it can be interpreted by non-experts. The classifier outputs can be used by non-experts to decide if the patient needs to be checked by a specialist. It can resolve partially the problem of having a shortage of specialists and it can be the first step towards a more sophisticated algorithm to help experts of the audiology field. [PERSON] et al., used deep learning convolutional neural network architecture to classify audiograms of normal hearing, sensorineural, conductive, and mixed hearing loss. The audiograms were converted to jpeg formatted picture files. Image transformation techniques were used to increase the number of images available as a training data for the classifier. Image rotation, wrapping, contrast, lighting and zoom were applied to the audiogram images in the training set. They achieved 97.5% accuracy of their model to classify hearing loss types based on features extraction of the audiograms [13]. In this research, the study aimed at classifying audiograms to detect the cause of hearing loss which is not helping in configuring hearing aids or not conducted for this purpose [13].