The prediction of bankruptcy risk poses a formidable challenge in the fields of economics and finance, particularly within the healthcare industry, where it carries significant economic implications. The burgeoning field of healthcare electronic commerce, continuously evolving through technological advancements and changing regulations, introduces additional layers of complexity. We collected financial data from 1265 U.S. healthcare industries to predict bankruptcy based on 40 financial ratios using multi-class classification machine learning models across various industry subsectors and market capitalizations. The exceptionally high post-tuning accuracy rates, exceeding 90%, along with high-performance metrics solidified the robustness and exceptional predictive capability of the gradient boosting model in bankruptcy prediction. The results also demonstrate the power and sensitivity of financial ratios in predicting bankruptcy based on financial ratios. The Altman models highlight the return on investment (ROI) as the most important parameter for predicting bankruptcy risk in healthcare industries. The Ohlson model identifies return on assets (ROA) as an important ratio specifically for predicting bankruptcy risk within industry subsectors. Furthermore, it underscores the significance of both ROA and the enterprise value to earnings before interest and taxes (EV/EBIT) ratios as important parameters for predicting bankruptcy based on market capitalization. Recognizing these ratios enables proactive decision making that enhances resilience. Our findings contribute to informed risk management strategies, allowing for better management of healthcare industries in crises like those experienced in 2022 and even on a global scale.

The present study aims to use enhanced ionic polymer–metal composites (IPMC) as an artificial muscle (a soft-active actuator) to restore eyelid movement of patients with ptosis. The previous eyelid movement mechanisms contained drawbacks, specifically in the lower eyelid. We used finite element analysis (FEA) to find the optimal mechanism among two different models (A and B). In addition to common electrodes of IPMC (gold and platinum), the bovine serum albumin (BSA) and microcrystalline cellulose (MCC) polymers, with optimal weight percentages of carbon nanotube (CNT) nanofiller, were also utilized as non-metallic electrodes to improve the efficiency of the IPMC actuator. In both models, IPMC with nanocomposite electrodes had higher efficiency as compared to the metallic electrodes. In model A, which moved eyelids indirectly, IPMC with MCC-CNT electrode generated a higher force (25.4%) and less stress (5.9 times) as compared to IPMC with BSA-CNT electrode. However, the use of model A (even with IPMCs) with nanocomposite electrodes can have limitations such as possible malposition issues in the eyelids (especially lower). IPMC with MCC-CNT nanocomposite electrode under model B, which moved eyelids directly, was the most efficient option to restore eyelid movement. It led to higher displacements and lower mechanical stress damage as compared to the BSA-CNT. This finding may provide surgeons with valuable data to open a window in the treatment of patients with ptosis.

Breast cancer is the most prevalent form of cancer and the primary cause of cancer-related mortality among women globally. Breast cancer diagnosis involves multiple variables, making it a complex process. Therefore, the accurate estimation of features for diagnosing breast cancer is of great importance. The present study used a dataset of 21 patients with carcinoma breast cancer. Polynomial regression analysis was used to non-invasively estimate six impedance features for the diagnosis of breast cancer, including the phase angle at 500 KHz (PA500), impedance distance between spectral ends (DA), area normalized by DA (A/DA), maximum of the spectrum (Max IP), the distance between impedivity (ohm) at zero frequency and the real part of the maximum frequency point (DR), and length of the spectral curve (P). The results indicated that the polynomial degrees needed to estimate the PA500, DA, A/DA, Max IP, DR, and P features based on tumor size were 2, 2, 3, 3, 2, and 2, respectively. Additionally, we utilized a nonlinear constrained optimization (NCO) analysis to calculate the eight threshold levels for the classification of the impedance features. The deduction of eight classifications for each feature may also be an effective tool for decision-making in breast cancer. These findings may help oncologists to estimate the impedance features for breast cancer diagnosis non-invasively.