Polycystic ovary syndrome (PCOS) and endometrial carcinoma (EC) are gynecological conditions that have attracted significant attention due to the higher prevalence of EC in patients with PCOS. Even with this proven association, little is known about the complex molecular pathways that connect PCOS to an increased risk of EC. In order to address this, our study presents two main innovations. To provide a solid basis for our analysis, we have first created a dataset of genes linked to EC and PCOS. Second, we start by building fixed-size ensembles, and then we refine the configuration of a single clustering algorithm within the ensemble at each step of the hyperparameter optimization process. This optimization evaluates the potential performance of the ensemble as a whole, taking into consideration the interactions between each algorithm. All the models in the ensemble are individually optimized with the suitable hyperparameter optimization method, which allows us to tailor the strategy to the model’s needs. Our approach aims to improve the ensemble’s performance, significantly enhancing the accuracy and robustness of clustering outcomes. Through this approach, we aim to enhance our understanding of PCOS and EC, potentially leading to diagnostic and treatment breakthroughs.

Hashimoto’s thyroiditis is an autoimmune disorder characterized by the destruction of thyroid cells through immune-mediated mechanisms involving cells and antibodies. The condition can trigger disturbances in metabolism, leading to the development of other autoimmune diseases, known as concomitant diseases. Multiple concomitant diseases may coexist in a single individual, making it challenging to diagnose and manage them effectively. This study aims to propose a novel hybrid algorithm that classifies concomitant diseases associated with Hashimoto’s thyroiditis based on sequences. The approach involves building distinct prediction models for each class and using the output of one model as input for the subsequent one, resulting in a dynamic decision-making process. Genes associated with concomitant diseases were collected alongside those related to Hashimoto’s thyroiditis, and their sequences were obtained from the NCBI site in fasta format. The hybrid algorithm was evaluated against common machine learning algorithms and their various combinations. The experimental results demonstrate that the proposed hybrid model outperforms existing classification methods in terms of performance metrics. The significance of this study lies in its two distinctive aspects. Firstly, it presents a new benchmarking dataset that has not been previously developed in this field, using diverse methods. Secondly, it proposes a more effective and efficient solution that accounts for the dynamic nature of the dataset. The hybrid approach holds promise in investigating the genetic heterogeneity of complex diseases such as Hashimoto’s thyroiditis and identifying new autoimmune disease genes. Additionally, the results of this study may aid in the development of genetic screening tools and laboratory experiments targeting Hashimoto’s thyroiditis genetic risk factors. New software, models, and techniques for computing, including systems biology, machine learning, and artificial intelligence, are used in our study.

The growing incidence of Non-Hodgkin lymphomas (NHL) in recent times has brought attention to the need for thorough investigations of their genetic associations with autoimmune and rheumatologic conditions, such as systemic lupus, celiac disease, and Sjögren’s syndrome. Our study is the first of its type in this field since it uses machine learning to investigate these relationships in great detail. Firstly, we have developed a new genetic dataset, specifically designed to uncover the genetic intricacies of NHL and rheumatologic diseases, offering unprecedented insights into their molecular mechanisms. Following this, we introduced the Clustered-Based Binary Grey Wolf Optimizer (CB-BGWO), a novel method that significantly revolutionizes the feature selection process in genetic analysis. This optimizer significantly improves the accuracy and efficiency of identifying important genetic variables affecting the interaction between rheumatologic and NHL illnesses. This methodological advance not only increases the analytical power but also creates a new standard for genetic research methods. Our findings address a significant gap in the literature and offer valuable insights that could positively support future treatment strategies and research paths. By illuminating the complex genetic connections between NHL and significant rheumatologic conditions, this work contributes to a better understanding and treatment of these complex diseases.

The application of machine learning in predicting earthquake magnitudes is crucial due to its ability to process extensive data sets and identify intricate patterns, thereby enhancing the accuracy and timeliness of predictions. This capability is essential for improving readiness and relief techniques against seismic activities. This study introduces a novel hybrid ensemble model, the HEM NAEMP, specifically evolved for predicting earthquake magnitudes along the North Anatolian Fault zone. The model integrates data from both the North Anatolian and San Andreas fault zones-the latter selected due to its tectonic similarity-to develop a comprehensive dataset that includes newly extracted features. The novelty of this study lies in the combination of data from two different fault lines to create a new dataset, the extraction of novel features, and the development of a previously unused model leveraging this dataset and its features. The HEM NAEMP model employs a several of regression algorithms, including k-nearest neighbors, random forest, support vector machine, decision tree and extreme gradient boosting, to effectively predict earthquake magnitude. The evaluation metrics for the model are as follows: mean squared error (MSE) of 0.011, mean absolute error (MAE) of 0.064, root mean squared error (RMSE) of 0.108, mean absolute percentage error (MAPE) of 0.268, R Square $$(\text {R}^2)$$ ( R 2 ) of 0.92 and training time of 2.44 sec. These results are compared against those from a Long-Short Term Memory (LSTM), Convolutional Neural Network (CNN) and AutoRegressive Integrated Moving Average (ARIMA) models, demonstrating that HEM NAEMP has mostly lower error rates in MAE and MAPE and high score in $$\text {R}^2$$ R 2 , as well as reduced training time, thereby confirming its viability and efficiency.