Cost Factors Research Articles

An Overview of the Need for Circular Economy on Electric Vehicle Batteries

Batteries are a widely utilized and simple method for powering electronic devices, particularly given the prevalence of individuals traveling to all gadgets. The escalating adoption of electric vehicles and portable electronic devices has led to a surge in the demand for lithium-ion batteries. Consequently, this has given rise to supply uncertainties in acquiring essential minerals such as lithium and cobalt, along with concerns about the proper disposal of dead batteries. The existing methods for battery recycling exhibit variations based on the individual chemistries of the batteries, hence influencing both cost factors and greenhouse gas emissions. Simultaneously, there exists a possibility for repurposing depleted batteries for low-tier energy storage applications. The absence of legislation pertaining to the secure storage and handling of waste streams contributes to the accumulation of refuse in exposed environments and the release of hazardous substances from landfills. In addition, contemporary battery manufacturing methods necessitate the utilization of innovative substances, such as ionic liquids for electrolytes and nanostructures for cathodes, to enhance the energy characteristics and longevity of batteries. The presence of uncertainties regarding the accurate assessment of the environmental consequences associated with novel battery chemicals has the potential to impede efforts aimed at recycling and containment. The objective of this analysis is to consolidate the existing knowledge regarding battery pollutants, both those that are recognized and those that remain uncertain, and to assess their potential environmental impacts. Additionally, this research aims to examine the current strategies and methods employed for the recycling of batteries in the circular economy.

Direct non-medical cost and associated factors in treatment of type 2 diabetes with complications at several hospitals in Ho Chi Minh city

Introduction: Type 2 diabetes mellitus (T2DM), especially those with complications, requires lifelong care and incurs substantial expenses. In order to reduce out-of-pocket costs borne by patients, understanding costs including direct non-medical cost is important. This study aims to examine direct non-medical cost and related factors among T2DM with complications. Methods: A cross-sectional survey of 617 T2DM patients conducted at Thong Nhat and Nguyen Tri Phuong hospitals from May 2023 to July 2023 using convenience sampling method. Generalized linear regression models were used to examine the effect of having complications on direct non-medical cost and other associated factors. Results: The average direct non-medical costs amounted to 1,351,706 VND. For individuals with complications, the average direct non-medical cost was 1,716,756 VND, nearly 1.81 times higher than those without complications. Regarding type of complications, having both complications incurred higher cost than those without complications (245631.8, 95%CI: 29952.3-461311.4, p=0,026), adjusted for other factors. The factors associated with direct non-medical costs included place of residence, educational attainment, having health insurance, traveling with a family member, using personal or public transport, using supplementary foods, and the patient’s income (p < 0.05). Conclusion: This study, among the first to analyze differences in direct non-medical costs among T2DM groups with complications in Vietnam, could contributes to understanding the economic dimensions of T2DM and provide valuable insights for future interventions aimed at alleviating the economic burden of T2DM on patients and society. Keyword: Direct non – medical cost, type 2 diabetes, complication, associated factors.

Faktor Penghambat Penggunaan Tracer Pada Unit Penyimpanan Berkas Rekam Medis Pasien Rawat Jalan di Rumah Sakit

Medical record tracers are a replacement for medical records that will be removed from storage for any purpose, usually made of strong and colored material. Factors that cause delays in the use of tracers in storage rooms include the lack of staff with educational backgrounds in medical records, the absence of special officers in the storage department and the non-implementation of SOPs regarding the use of tracers. The aim of this research is to determine the factors inhibiting the use of tracers in the outpatient medical record storage unit at Bhayangkara Hospital Banda Aceh in 2023. This research was conducted in the outpatient storage room at Bhayangkara Hospital Banda Aceh, 15-20 May 2023. This type of research is Descriptive Qualitative. The population of this study consisted of 9 medical records officers. The research sample consisted of 3 people specifically for outpatient medical records officers using Purposive Sampling Technique. The results of the research show that in the use of tracers in the outpatient medical record file storage unit, the human resource factor is in the "insufficient" category, the facilities and infrastructure factor is in the "incomplete" category, the cost factor is in the "not available" category, the equipment and machine factor is in the "not available" category. available” and the raw material factor is in the “incomplete” category. Meanwhile, the factors inhibiting the use of Tracer in storing outpatient medical record files are in the "existing" category. It is recommended to add tracer printing equipment in the form of a printer in the storage room, adding officers who have medical records degrees so that they can carry out outpatient storage properly.

Package-type strategies and packaging's carbon reduction decisions in the take-out industry

Using environmentally friendly packaging (E-packaging) instead of non-environmentally friendly packaging (N-packaging) can mitigate pollution and reduce carbon emissions. However, the market penetration rate of E-packaging is low, and the packaging carbon reduction level is uneven. This study uses game theory models to examine the factors affecting packaging carbon reduction levels and fees as well as firms' package-type decisions. First, we obtain the optimal solutions for packaging manufacturers and firms when they use different types of packaging under monopoly and competition, respectively. We find that green competition reduces the carbon reduction level of E-packaging, whereas price competition can increase it under certain conditions. The unit manufacturing cost of N-packaging and cost coefficient of carbon reduction are critical considerations for package-type decisions. When the unit manufacturing cost of N-packaging is low and the cost coefficient is large, no firm uses E-packaging. We also demonstrate that firms in a competitive market may become trapped in a prisoner's dilemma after making package-type decisions. Interestingly, we show that the firms' package-type decisions can hurt consumers and welfare under certain conditions, depending on the N-packaging's unit manufacturing cost and pollution damage factor. Our findings provide insights for packaging manufacturers, firms, and environmental regulatory agencies, and can help promote E-packaging.

Bayesian estimation of equipment reliability with normal-type life distribution based on multiple batch tests

Abstract The test of new equipment is usually carried out in multiple batches according to the task schedule and test results. Constrained by the test environment, cost, and other factors, the amount of reliability test data in each batch is relatively limited, which brings difficulties to the accurate equipment reliability estimation work. For the reliability simulation tests conducted before each batch tests, it is particularly important to make full use of each batch tests information and simulation tests information to estimate the reliability of the equipment for small sample tests. This study takes the common normal-type life distribution equipment as the research object, and selects the normal-inverse gamma distribution as the equipment life parameters prior distribution based on the Bayesian method. Combined with the system contribution, the fusion weights of each batch tests information are determined and all the batch tests information is fused. Finally, the estimation of equipment reliability based on multiple batch tests is completed. The research results show that this method can integrate the information of each batch test and simulation test, overcome the problem of insufficient information of single batch tests, and provide an effective analytical tool for equipment reliability estimation.

금융 메타버스 플랫폼의 혁신저항과 수용의도에 관한 연구

Recently, as the metaverse market has grown rapidly, there has been a movement to strategically link metaverse in various fields such as distribution, manufacturing, and finance. The financial sector is actively using the metaverse platform and is conducting executive meetings, opening virtual branches, training new employees, and providing financial education services on the financial metaverse platform. The number of financial companies providing services linked to the metaverse is increasing due to expectations that the financial metaverse platform can improve information delivery of financial products and customer convenience. In particular, because financial consumers can use financial services without visiting a store through metaverse, financial companies are also accelerating the construction of their own metaverse platforms. In this way, the financial metaverse platform goes beyond the limitations of smartphones and is fundamentally changing the work methods, customer needs, and services of the financial industry based on the technical characteristics of online and offline connectivity.
 The purpose of this study is as follows. From the perspective of financial consumers, literature research and metaverse-related expert interview survey methodology were used to derive the benefits gained by accepting the metaverse platform and the sacrifice factors such as investment losses and security risks that occur to acquire value. As a result of the study, the utility factors gained by accepting the financial metaverse platform include perceived relative advantage, perceived suitability, and perceived usability. Cost factors included perceived privacy risk and perceived complexity. In addition, perceived usefulness, perceived enjoyment, and remote presence were presented as benefit factors of the financial metaverse platform service, and technical complexity, perceived cost, and perceived security risk were presented as sacrifice factors.

ANALISIS FAKTOR PENYEBAB SISWA/SISWI KELAS XII MEMILIH PROGRAM STUDI DI UNIVERSITAS TRIBUANA KALABAHI

This research aims to determine the factors that cause someone to choose a study program option At Tribuana Kalabahi University, several variables are analyzed, namely Job Opportunities, Study Costs, Parental Influence, Personal Interests, Study Program Image. Thisi research is quantitative research using descriptive methods explorative. The population in this study were Class XII SMA/SMK students in Regency Alor, the sample in this study was taken as many as 188 high school and vocational school students XII which is in Alor district with sampling techniques done properly random. Data Which collected in study use technique spread questionnaire study to class XII students SMA/SMK in Alor District. Technique The data analysis used in this research is statistical analysis descriptive percentage. The research results show firstly, the main factor that has a significant impact on the decision of high school/vocational school students to choose Tribuana Kalabahi University as a place to continue their higher education is ithe availability of access or ease of location, with an influence level reaching 55%; secondly, the Primary School Teacher Education Study Program (PGSD) dominates by receiving the highest support from respondents, reaching a percentage of 34%; third, the employment opportunity factor has a significant influence, with 81% of respondents, the study cost factor also has a large impact, reaching a score of 77%. Parental influence is also quite significant, with 69%. Personal iinterest actors had a slightly smaller but still quite relevant role, with a score of 58%. The image ofithe study program, although still irelevant, apparently has a lower influence compared to other factors, namely only 44%.

Development of a new damping ratio prediction model for recycled aggregate concrete: Incorporating modified admixtures and carbonation effects

Abstract Recycled aggregate concrete (RAC) has been widely used in practical engineering construction. However, the ability of buildings to resist wind-induced vibration and earthquake effects plays an important role in building safety. It is urgent to ensure that recycled concrete still has good anti-vibration ability within the allowable strength range. By conducting damping tests on recycled concrete specimens, the results show that the damping performance of RAC is better improved compared with natural aggregate concrete. Moreover, the influence of internal factors of recycled aggregates and external environmental conditions on damping performance can be determined, and corresponding damping ratio prediction models can be constructed. However, the current prediction models still have limitations in theory and practice. The existing damping ratio prediction models have a large span of independent variables and do not consider the gradual carbonation effect in the actual environment over time. To overcome these limitations, a new damping ratio prediction model is proposed. Based on the replacement rate of recycled aggregates (RAs) and the amplitude of excitation force, the influence of modified admixtures and carbonation on damping performance is considered, and the corresponding model prediction formula is constructed. In addition, the influence mechanism is further demonstrated and explained from the macroscopic aspect of specimen profile and the microscopic aspect of electron microscopy tests. It is found that, considering both strength and cost factors, recycled concrete still has good damping performance when the replacement rate of recycled aggregates (RAs) is 40%.

Prediction of heavy-section ductile iron fracture toughness based on machine learning

The preparation process and composition design of heavy-section ductile iron are the key factors affecting its fracture toughness. These factors are challenging to address due to the long casting cycle, high cost and complex influencing factors of this type of iron. In this paper, 18 cubic physical simulation test blocks with 400 mm wall thickness were prepared by adjusting the C, Si and Mn contents in heavy-section ductile iron using a homemade physical simulation casting system. Four locations with different cooling rates were selected for each specimen, and 72 specimens with different compositions and cooling times of the heavy-section ductile iron were prepared. Six machine learning-based heavy-section ductile iron fracture toughness predictive models were constructed based on measured data with the C content, Si content, Mn content and cooling rate as input data and the fracture toughness as the output data. The experimental results showed that the constructed bagging model has high accuracy in predicting the fracture toughness of heavy-section ductile iron, with a coefficient of coefficient (R2) of 0.9990 and a root mean square error (RMSE) of 0.2373.

AREA BASE OF COST ESTIMATION FOR BUILDING CONSTRUCTION PROJECTS USING AN ARTIFICIAL NEURAL NETWORK

Managers of projects monitor the project schedule and compare planning values with the actual cost into the project and how much of it is earned value. One of success methods for managing the construction projects is to find the effective cost factors and investigate the correlations between them. In order to determine the discrepancies, the Execution Phase performance measures are compared to the baseline metrics decided upon in the Planning Phase. The significance of these deviations is assessed by factoring them into the control methods at every stage. For that, the present paper developed an ANN technique for project management to monitor the cost of project based on the correlation between the project size and the project cite area within the implementation process. The contribution in this paper is to present a project planning cost mimic the real actual cost. Modifications to the project can be monitored using this method, which takes into account both the nature of the work being done and the time frame during which it is being performed. To gauge the system's efficacy, the ANN system was applied to structural concrete and building walls. The system's final output demonstrated a straightforward and reliable method of tracking and observing progress.

Research on the technology of intelligent vehicle sensor positioning system in autonomous driving

In this comprehensive exploration of positioning and navigation technologies, we have delved into the intricacies of GPS (Global Positioning System), IMU (Inertial Measurement Unit), and VPS (Visual Positioning Systems) systems, shedding light on their unique attributes and applications. GPS, a global satellite-based system, offers precise positioning on a worldwide scale, although it encounters challenges in complex urban and environmental conditions. Its role in navigation and tracking is undeniable. IMU, characterized by accelerometers and gyroscopes, excels in delivering high-precision positioning over short durations, making it invaluable for applications in aviation, robotics, and virtual reality. However, it is susceptible to drift over time. Visual Positioning Systems (VPS), harnessing computer vision and visual sensors, provide remarkable sub-meter to centimeter-level accuracy when conditions are optimal. Their significance is particularly pronounced in indoor navigation, augmented reality, and robotics, although they may face challenges in less favorable environments. These technologies are not isolated but can synergize to enhance accuracy and reliability. GPS and IMU collaborate to compensate for signal disruptions, while GPS and VPS join forces to tackle urban complexities. IMU and VPS integration offer precise indoor navigation and augmented reality experiences, delivering impeccable positioning and orientation data. Ultimately, the choice of technology hinges on specific application requirements, encompassing accuracy, environmental considerations, cost factors, and the need for complementary systems. As these technologies advance, they hold the promise of revolutionizing navigation across various domains, from autonomous vehicles to immersive augmented reality environments.

An innovative method for building electricity energy management in smart homes based on electric vehicle energy capacity

The surging demand for electricity, fueled by environmental concerns, economic considerations, and the integration of distributed energy resources, underscores the need for innovative approaches to smart home energy management. This research introduces a novel optimization algorithm that leverages electric vehicles (EVs) as integral components, addressing the intricate dynamics of household load management. The study’s significance lies in optimizing energy consumption, reducing costs, and enhancing power grid reliability. Three distinct modes of smart home load management are investigated, ranging from no household load management to load outages, with a focus on the time-of-use (ToU) tariff impact, inclining block rate (IBR) pricing, and the combined effect of ToU and IBR on load management outcomes. The algorithm, a multi-objective approach, minimizes the peak demand and optimizes cost factors, resulting in a 7.9% reduction in integrated payment costs. Notably, EVs play a pivotal role in load planning, showcasing a 16.4% reduction in peak loads and a 7.9% decrease in payment expenses. Numerical results affirm the algorithm’s adaptability, even under load interruptions, preventing excessive increases in paid costs. Incorporating dynamic pricing structures like inclining block rates alongside the time of use reveals a 7.9% reduction in payment costs and a 16.4% decrease in peak loads. In conclusion, this research provides a robust optimization framework for smart home energy management, demonstrating economic benefits, peak load reduction potential, and enhanced reliability through strategic EV integration and dynamic pricing.

Understanding Mobile OTT Service Users' Resistance to Participation in Wireless D2D Caching Networks.

With the explosive pace of mobile over-the-top (OTT) video content streaming services, mobile network traffic has seen unprecedented growth in recent years. However, the limitation of antenna performance, the burden of investment cost, and restricted resources hinder improving the current mobile networks' functionality. Accordingly, wireless device-to-device (D2D) caching networks came to the fore as one of the competitive alternatives for alleviating the overloads of mobile network traffic. Wireless D2D caching networks can be a desirable alternative for OTT service providers and telecommunication operators, but the problem is user resistance. User participation is imperative to deliver wireless D2D caching network functionality successfully. Thus, to gain a deeper understanding of user resistance toward wireless D2D caching networks and their underlying sources, this study introduces two perceived cost factors contributing to this resistance and one perceived benefit that mitigates such resistance. Based on an online survey, this study found new theoretical links among perceived costs and benefits, resistance, and participation intention. The findings reveal that user resistance is predicted by perceived costs, encompassing resource sacrifices and privacy concerns, whereas perceived benefits-specifically, perceived usefulness-did not significantly influence resistance. This implies that telecommunication operators should prioritize market requirements over technological advantages, emphasizing the potential for successful commercialization of wireless D2D caching networks.

Use of ecosystem services and land ownership to prioritize conservation areas on Jeju Island, Republic of Korea

In the face of ecological challenges, sustainable implementation of conservation strategies necessitates a delicate balance between ecosystem services, biodiversity, land ownership, and cost considerations. This study presents a conservation strategy for Jeju Island, Republic of Korea, an area renowned for its unique ecological features. We developed the strategy by evaluating 12 scenarios involving the establishment of protected areas (PAs) and the use of other effective area-based conservation measures (OECMs). The purpose of this evaluation was not to enhance ecosystem services and biodiversity directly but rather to identify strategies that could achieve these goals in a financially feasible way, considering the various cost and land ownership factors. Our findings revealed that conservation prioritization targets are primarily located in the central and eastern regions of Jeju Island, where ecosystem services are concentrated, and levels of biodiversity are high. The expansion of conservation targets from 17% to 30% entailed increased costs, largely due to the increased inclusion of private lands and agricultural areas. The introduction of OECMs provided a means to improve ecological network and representation within conservation networks. We also found that applying penalties to improve the boundary lengths of PAs could lead to more cost-effective conservation strategies. Although expanding conservation targets to 30% led to significant shifts in land ownership patterns and increased conservation costs, the benefits to biodiversity and provision of ecosystem services were substantial. This study provided valuable insights into the determination of conservation planning through the harmonization of biodiversity prot ection, provision of ecosystem services, and consideration of economic costs related to patterns of land use and ownership. The results can aid policymakers and stakeholders in making informed decisions about resource allocation in biodiversity conservation.

Multi-objective optimization of the hybrid photovoltaic-battery-diesel-desalination system based on multi-type of desalination unit.

Meeting the energy and water demands of remote areas has created significant challenges globally. To address this issue, the utilization of hybrid energy-water systems, integrated with renewable energies, has been highlighted as a viable solution. This work has been focused on the multi-objective optimization of a hybrid energy system, encompassing photovoltaic panels, batteries, diesel generators, and desalination units. The design goals to achieve the optimal configuration include minimizing system costs, reducing carbon dioxide emissions, enhancing the renewable factor, and improving reliability. Also, for the mentioned design goals, the performance of three desalination methods including reverse osmosis (RO), multi-stage flash (MSF), and multiple-effect distillation (MED) was evaluated by Hybrid Optimization of Multiple Energy Resources (HOMER) software. Our findings reveal that the RO desalination method, when combined with renewable energy, outperforms other methods both economically and environmentally. Notably, the RO method reduces net present cost (NPC) by 6.18% and 8.25% and carbon dioxide emissions by 38% and 46%, respectively, compared to MED and MSF methods. Additionally, sensitivity analysis, considering factors such as interest rate, photovoltaic panel cost, battery cost, and fuel cost, was conducted on NPC. The results showed that with a 2% decrease in the interest rate, the amount of NPC increases by about 2.4% due to the increase in the share of renewable energy. Therefore, reducing the interest rate helps to design a system with less carbon dioxide emissions. This work, by highlighting the economic and environmental implications of different desalination methods, as well as key cost factors, contributes to the optimal design of combined energy-water schemes for remote areas.

An Empirical Study on Public Sector versus Third Sector Circular Economy-Oriented Innovations

Extensive research has explored organisational dynamics across various sectors in relation to circular economy (CE) innovation practices. However, a critical gap exists in understanding CE innovation activities in the public sector versus the third sector. This distinction is crucial as the third sector’s role in CE innovation is growing, necessitating tailored policies instead of homogenous integration into public sector-based approaches. We address this gap by focusing on nuanced comparisons between the public and third sectors, delving into the motivations and constraints surrounding CE innovations across these different sectors. Employing an exploratory sequential design, we integrate qualitative insights from 12 interviews with quantitative measures derived from 153 survey responses within CE innovation communities. The findings reveal subtle yet significant disparities in innovation activities between the public and third sectors within a CE innovation community. Notably, cost factor differences related to the availability of finance and knowledge factors associated with the lack of information on technology emerge. This research contributes evidence-based insights, offering practitioners and policymakers a nuanced understanding of the motivations and constraints of circular economy innovation. These findings can be instrumental in steering the transition towards a more sustainable and circular economy, emphasising the need for tailored strategies rather than a one-size-fits-all approach.

An extendable voltage boosting gain unit with a self‐balanced switched‐capacitors multilevel inverter using a single DC source

SummaryThis paper suggests an extendable voltage boosting gain unit (EVBGU) using switched‐capacitors SC with a single DC source. The proposed unit can be extended by a modular pattern to increase the output voltage levels and voltage‐boosting gain of the inverter. The main feature of this work is presenting a unit topology that utilizes multiple and modular subcells to achieve (double, quadruple, hexapole, octapule, etc.) voltage gain at the output by increasing the number of the subcells. All the subcells are connected only with one DC source. A positive feedback technique has been used to charge the capacitors of the unit using the accumulative charging voltage (ACV) method. Furthermore, all the capacitors are self‐balanced, so there is no need for any additional and complex control circuit. Moreover, to decrease the cost factor of the inverter, a low IGBT switch count is taken into consideration and no passive elements such as diodes are used in the proposed structure. The H‐bridge is connected to the unit to achieve bipolar voltage at the output. The operational analysis of the inverter is delineated in detail. A comparative study, with state‐of‐the‐art MLIs in different terms such as the number of components, the total standing voltage, and the gain, shows the particularity of the proposed EVBGU. The simulation of the proposed MLI is performed using PSCAD‐EMTDC software. The simulation results are validated experimentally.

After intensive care therapy : Outpatient intensive nursing care

Outpatient intensive nursing care offers individuals who require specialized intensive nursing care on a permanent basis the opportunity to survive outside of an intensive care unit in an ambulatory environment. With advancements in surgical and intensive medical practices, coupled with demographic changes in the population resulting in a higher number of older, multimorbid patients, the available treatment options have been continuously extended. Many patients survived their intensive care stays but could not be weaned from ventilation or they had to retain a tracheal cannula, even without ventilation therapy. Presently, this group constitutes the majority of patients receiving outpatient intensive nursing care in Germany and represents a substantial cost factor in healthcare. The care of these patients is governed by the Guideline of the Joint Federal Committee on the Prescription of Outpatient Intensive Nursing Care (AKI-RL), which is based on the Intensive Care and Rehabilitation Strengthening Act (GKV-IPReG) 2021.

Understanding user perceptions toward marketing in the metaverse

PurposeThis study develops a comprehensive research model and investigates the significant factors affecting positive marketing outcomes in the Metaverse through perceived social benefits and trust.Design/methodology/approachThe authors propose a new research model based on social exchange theory (SET) and examine the impact of cost and reward factors. Using 327 survey samples collected from current Metaverse users in South Korea, dual-stage analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) and an artificial neural network (ANN) were employed to test the study’s hypotheses.FindingsThe results showed that perceived social benefit and trust had significant mediating effects on marketing outcomes, such as loyalty to the seller, product/service attitude, and purchase intention. All antecedents, except perceived performance risk, had a crucial impact on the two mediators. The most interesting finding of this study is the positive influence of knowledge-seeking efforts on perceived social benefits.Originality/valueThis study is the first empirical research to examine the effectiveness of marketing in the Metaverse. It also proposes a new theoretical model based on SET to investigate users’ behavioral intentions regarding marketing in the Metaverse, and confirms its explanatory power. Moreover, the results of this study also offer suggestions to brands on how to market to consumers in the Metaverse.

Prediction and the influencing factor study of colorectal cancer hospitalization costs in China based on machine learning-random forest and support vector regression: a retrospective study.

As people's standard of living improves, the incidence of colorectal cancer is increasing, and colorectal cancer hospitalization costs are relatively high. Therefore, predicting the cost of hospitalization for colorectal cancer patients can provide guidance for controlling healthcare costs and for the development of related policies. This study used the first page of medical record data on colorectal cancer inpatient cases of a tertiary first-class hospital in Shenzhen from 2018 to 2022. The impacting factors of hospitalization costs for colorectal cancer were analyzed. Random forest and support vector regression models were used to establish predictive models of the cost of hospitalization for colorectal cancer patients and to compare and evaluate. In colorectal cancer inpatients, major procedures, length of stay, level of procedure, Charlson comorbidity index, age, and medical payment method were the important influencing factors. In terms of the test set, the R2 of the Random forest model was 0.833, the R2 of the Support vector regression model was 0.824; the root mean square error (RMSE) of the Random forest model was 0.029, and the RMSE of the Support vector regression model was 0.032. In the Random Forest model, the weight of the major procedure was the highest (0.286). Major procedures and length of stay have the greatest impacts on hospital costs for colorectal cancer patients. The random forest model is a better method to predict the hospitalization costs for colorectal cancer patients than the support vector regression.