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Objective: This study aims to explore the use of machine learning algorithms for predicting disease classification. Methods: An integrated algorithm (KPLSKELM) was proposed in this study. The algorithm employed kernel principal component analysis to transform the original data into a high-dimensional feature space, thereby enhancing its linear separability. It used the sparrow search algorithm (SSA) to optimize the weight matrix and parameters of the kernel extreme learning machine (KELM). The algorithm incorporated a Gaussian perturbation search mechanism to refine the population initialization strategy so as to mitigate the issues of poor convergence rate and susceptibility to local optima in the later SSA iterations. Lévy flight perturbations were introduced during the foraging search process of the sparrow population to guide the population in moving appropriate step sizes, thereby increasing the diversity of the spatial search. The proposed method was experimentally validated using a binary classification breast cancer dataset collected by Dr. William H. Wolberg from a Wisconsin hospital in the United States and a multiclass classification dataset of electrocardiographic recordings during childbirth. Multiple metrics were adopted to evaluate the classification performance. Results: The accuracy and F1_score of the KELM model remained relatively low across different percentages of the training set, although a recall of 1.0000 was consistently achieved. Both the SSA-improved KELM and the Lévy-improved SSA-optimized KELM algorithms exhibited better performance in terms of the comprehensive metric F1_score and improved with the increase in the percentage of the training set. The KPLSKELM model outperformed others in all metrics, with accuracy, precision, recall, and F1_score approaching or reaching the highest levels when using 90% of the training set. Conclusions: The proposed method demonstrated excellent performance in various disease prediction tasks, holding high practical application value. It provided a reference for further assisting clinicians in making more precise treatment decisions.

Based on the pharmaco-physiology of the aminobisphosphonates, it could be speculated that bisphosphonates could induce not only the osteopetrotic bone disease because of their selective suppression of osteoclastic activity, but also could affect directly or indirectly the endocrine system, local factors, and also the bone metabolic turnover. Consequently, the bone fragility could be rather produced by long-term aminobisphosphonate therapy. Bisphosphonate-mediated bone disease was labeled by Odvina et al. in 2005 [Odvina CV, Zerwerth JE, Rao DS et al. Severely suppressed bone turnover; a potential complication of alendronate therapy. J Clin Endocrinol Metab90: 1294–1301, 2005.] as the "severely suppressed bone turnover (SSBT)" on the metabolic turnover basis. However, such definition could contain various drug-induced bone diseases, and did not indicate any particular condition, induced by the bisphosphonate. The term "SSBT" is thought not solely to be based on its histology, and seems rather a clinical term applicable to the various drug-induced bone diseases. Therefore, the current authors attempted to characterize the bisphosphonate-mediated bone disease on the basis of the combined image and histological studies, and finally concluded that the prolonged bisphosphonate therapy could produce an atypical osteomalacic bone disease. (osteosclerosis of osteomalacia) which leads to fragility fracture. It is puzzling as to why malacia rather than petrosis develops in the skeleton.

Mathematical modeling has proven to be a viable alternative for investigating the temperature distribution inside the human eye. This is due to its ability to overcome the limitations infrared (IR) thermography; the leading method in ocular temperature measurement. A wide range of mathematical studies on the ocular temperature distribution during various conditions have been published in the literature. In this paper, we carry out an in-depth review of the various mathematical models of the eye that have been developed in the past. Various problems and the implications from the mathematical predictions of these studies are discussed. The future directions of studies in ocular temperature distribution are deliberated.