## 1 Introduction  
The world is seeing a steady increase in ear diseases, affecting millions of children a year, as shown in Figure 1. Among them, the most prominent is middle ear infection, accounting for 46.3%. Hearing loss and a significant impact on quality of life can be caused by otitis media, a common ear infection in children. The reason for this infection is either the inflammation or blockage of the Eustachian tube, which often occurs because of the shorter and underdeveloped Eustachian tubes in children [1; 2; 3]. The diagnosis of otitis media is challenging because symptoms are often mild and unrecognized by the patient, such as slight hearing loss and discomfort [4]. This has the potential to cause delayed treatment and potential complications [5; 6]. Therefore, accurate diagnosis and early intervention are crucial to preventing hearing loss and its negative consequences on social interaction, education, employment, and overall well-being [7]. Different techniques are employed to diagnose the tympan membrane, but there is a lack of objective and precise diagnostic methods. The diagnostic methods that currently exist include clinical tests, hearing tests, and imaging tests. First, a clinical test is a doctor's visual examination of the symptoms of otitis media. A clinical examination can also be subjective and less precise. Second, hearing tests are used to check pediatric hearing loss due to otitis media. Children may experience inconvenience from time-consuming hearing tests. Finally, imaging tests are methods to generate images of the middle ear cavity using techniques such as CT (computed tomography) or MRI (magnetic resonance imaging). Imaging tests can be used to identify structural changes in otitis media, but they are expensive and pose a risk of radiation exposure. Recently, artificial intelligence technology, particularly deep learning [8; 9] technology among artificial neural networks [10], has been used to develop new diagnostic methods in the medical field. Deep learning technology is capable of creating predictive models by analyzing data like images, speech, and text. Various studies [11] of deep learning are also being conducted on the middle ear disease [12], and these studies are aimed at assisting in diagnosis. The average diagnostic accuracies of doctors [13] are 73% (Otolaryngologists) and 50% (Pediatrics). Since the diagnosis results and their precision vary from doctor to doctor, and since there is a possibility of bias [14], research on diagnostic assistance using deep learning is emerging to solve this problem. We propose a novel model for diagnosing tympan membrane disease and predicting pediatric hearing by combining the CNN (convolution neural networks) [15] and MLP (multi-layer perceptron) [16] models. Previous research has demonstrated that CNN models are highly effective in extracting and classifying features from medical images. In contrast, MLP models are effective in learning complex nonlinear relationships. This study covers the medical definitions of tympan membrane diseases and pediatric hearing. First, the OME (otitis media with effusion) disease is a condition in which non-infectious fluids accumulate in the middle ear. Second, congenital cholesteatoma disease is an abnormal skin growth that occurs in the middle ear canal. Third, traumatic perforation disease is the formation of a hole in the tympan membrane. Fourth, the COM (chronic otitis media) disease is the development of inflammation in the middle ear (middle ear) that lasts for more than 3 months. Fifth, AOM (acute otitis media) is the development of inflammation in the middle ear that lasts less than 3 weeks. Sixth, otitis externs is an inflammation that occurs in the ear canal (a passage behind the ear). Finally, pediatric hearing, which is affected by these diseases, refers to the ability to understand sounds and languages. The main goals of this study are as follows.