# Machine Learning Method for Prediction of Hearing Improvement After Stapedotomy
## 1 Introduction  
Otosclerosis is a localized disease of the bone derived from the otic capsule and characterized by alternating phases of bone resorption and formation. The prevalence of the disease in the Caucasian population is 0.3-0.4% of the general population. In 10% of these patients, the focus is localized near the oval window niche in the middle ear, leading to fixation of the steps with consecutive conductive or mixed hearing loss. Surgery is considered for patients with a conductive hearing loss of at least 15 dB in the frequencies 250 to 1000 Hz or higher. Before the surgery, patients should be informed of the risks of the operation, which include failure to improve hearing with residual conductive hearing loss, the possibility of sensorineural hearing loss, deafness, vestibular dysfunction, perforation of the tympanic membrane, and taste disturbance. A hearing test--audiogram--is always performed before the surgery and after the surgery. In the test, the surgeon observes whether hearing has improved. Following surgery, hearing may improve in both air conduction and bone conduction. The difference between bone and air conduction, known as the air-bone gap (ABG), is reduced. When advising patients before surgery, it is of great importance that the surgeon describes their own results. Predicting the result of the surgery would facilitate the patient's decision-making process and also help the surgeon to decide on the operation in doubtful cases. In preoperative counseling, it is important to present the surgeons' results, not the results found in the literature. The decision of the patient to undergo surgery is more straightforward when they are aware of the results of the operating surgeon. Prior to thesurgery, a pure tone audiogram is made, which is later compared with the postoperative pure tone audiogram, typically four or six weeks following the surgery. The objective was to develop a machine learning method that could predict the postoperative result of the operating surgeon's procedure and be presented to the patient prior to surgery in order to provide a better understanding of the likely final outcome. The use of artificial intelligence in medicine has become more and more prevalent in recent years. In the field of otology, there is ongoing research on the development of machine and deep learning methods to improve hearing assessment and assist with diagnosis and prognosis of otolaryngological diseases. In the field of hearing loss prediction, several studies predict the noise-induced hearing loss of industrial workers. Machine learning methods achieved high-quality results in these cases [1; 2; 3]. Both regression and classification methods were proposed, depending on whether the target class (hearing loss) was split into categories based on hearing intensity (dB) thresholds. Hearing recovery prediction methods were applied in cases of idiopathic sudden sensorineural hearing loss (ISSHL) [4; 5; 6; 7]. Multiple machine and deep learning methods were tested, with much experimentation on feature importance estimation and selection. Audiogram shape was the most important feature for prediction [7]. A numerical scoring system and a machine learning approach were compared in predicting the hearing outcome after tympanoplasty surgery [8]. Multiple preoperative features were collected to predict the outcome. Hearing improvement was categorized in several ways based on ABG improvement thresholds. Random forest provided promising results for ABG prediction, with preoperative ABG being the most decisive factor in prediction. One study [9] introduced a method for predicting recovery in patients with chronic otitis media who underwent canal-wall-down mastoidectomy. Among the models, decision tree and LightGBM were the best performing ones. Preoperative bone conduction hearing, age, and ABG were recognized as the most influential factors. To our knowledge, no method has been proposed for predicting the hearing outcome after stapedotomy.