# Part-Treatment Efficacy Prediction Using a CMA-ES-Based Dendritic Neuron Model
## 1 Introduction  
With the application of artificial intelligence in many fields of society, such as industry [1], agriculture [2], bioinformatics [3, 4], and biomedicine [5, 6], the world has witnessed great developments as a result of artificial intelligence technology [7]. Especially in the medical industry, artificial intelligence technology has significantly improved healthcare and reduced costs [8, 9, 10, 11]. Machine learning can combine medical data to generate appropriate predictive models. Excellent machine learning models can quickly and accurately predict diseases and assist doctors in making appropriate diagnoses for patients [12, 13]. Machine learning models have become highly adaptable in the field of computer-aided diagnosis in recent years [14, 15, 16]. Warts are growths caused by human papillomavirus (HPV). There are many different types of warts that can result in different degrees of harm to the body [17, 18, 19]. HPV also has the potential to induce cancer when it infects specific areas of the body [20]. Due to the impact of warts on patients' lives, they usually need urgent treatment. Current clinical treatments for wart dermatosis include cryotherapy, immunotherapy, and destructive therapy. Different patients suffering from the same type of wart skin disease can have varying responses to the same treatment because of different symptoms and individual differences [21]. The cost of treatment and the pain experienced by the patient during the treatment process vary from one treatment method to another [22]. Therefore, choosing the right method can save patients money and reduce their pain during treatment. However, in clinical practice, physicians usually choose a treatment method for their patients usingsubjective judgment. In many cases, patients may require multiple treatments before achieving a cure. The wart-treatment efficacy prediction problem, i.e., predicting whether a selected wart-treatment method is effective or not, remains a challenging task in computer-aided diagnosis. Machine learning methods can predict the appropriate treatment for wart patients, effectively eliminating their symptoms and avoiding repeated treatments. [PERSON] et al. used a rule-based fuzzy logic system to predict the efficacy of different treatments for warts [23]. [PERSON] et al. used an ID3 decision tree for wart-treatment efficacy prediction [24], where they converted the decision path generated by the decision tree into a fuzzy information graph. On the other hand, since data are key to machine learning-assisted medical diagnosis, [PERSON] et al. noticed that traditional machine learning models were less robust when performing wart-treatment efficacy prediction [25] because they could not effectively handle sample attributes with small values. To improve the accuracy of wart-treatment efficacy prediction, they proposed combining an adaptive particle swarm algorithm with an artificial immune recognition system to generate prediction models. The effect of data on prediction accuracy was similarly noted by [PERSON] et al. [26]. They developed a fuzzy-rough-KNN algorithm based on efficient data feature generation and selection. In addition, the data imbalance problem is very common in currently available medically relevant datasets. [PERSON] et al. used the Synthetic Minority Over-Sampling Technique (SMOTE) algorithm to balance raw data, addressing the data imbalance problem in wart-treatment efficacy prediction [27]. Although the above study improved the model from various perspectives to improve the accuracy of wart-treatment efficacy prediction, a machine learning model with higher accuracy and interpretability is still worthy of exploration by researchers. Using the dendritic neuron model as a machine learning model has attracted significant attention in recent years. [PERSON] et al. proposed using this model to address the classification problem [28] but noted that the performance of the model was limited due to the back-propagation algorithm easily falling into local convergence.