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In healthcare, a diagnosis is reached after a thorough physical assessment and analysis of the patient's medicinal history, as well as the utilization of appropriate diagnostic tests and procedures. 1.7 million People worldwide lose their lives every year due to complications from chronic kidney disease (CKD). Despite the availability of other diagnostic approaches, this investigation relies on machine learning because of its superior accuracy. Patients with chronic kidney disease (CKD) who experience health complications like high blood pressure, anemia, mineral-bone disorder, poor nutrition, acid abnormalities, and neurological-complications may benefit from timely and exact recognition of the disease's levels so that they can begin treatment with the most effective medications as soon as possible. Several works have been investigated on the early recognition of CKD utilizing machine-learning (ML) strategies. The accuracy of stage anticipations was not their primary concern. Both binary and multiclass classification methods have been used for stage anticipation in this investigation. Random-Forest (RF), Support-Vector-Machine (SVM), and Decision-Tree (DT) are the prediction models employed. Feature-selection has been carried out through scrutiny of variation and recursive feature elimination utilizing cross-validation (CV). 10-flod CV was utilized to assess the models. Experiments showed that RF utilizing recursive feature removal with CV outperformed SVM and DT.

This study aims to investigate the impact of R&D intensity on operational efficiency and strategic alliances in medium-sized companies. Specifically, it seeks to understand how these variables interact to influence a firm's commitment to research and development activities, ultimately affecting their innovation and market performance. A cross-sectional design was employed, with a sample of 230 participants drawn from medium-sized companies. The sample size was determined using the Morgan and Krejcie table. Data were collected through structured questionnaires assessing R&D intensity, operational efficiency, and strategic alliances. Pearson correlation analysis was conducted to examine the relationships between the dependent variable (R&D intensity) and each independent variable (operational efficiency and strategic alliances). Linear regression analysis was performed to explore the combined effect of the independent variables on R&D intensity. All analyses were conducted using SPSS version 27. Pearson correlation coefficients indicated significant positive relationships between R&D intensity and operational efficiency (r = 0.53, p = 0.001), and between R&D intensity and strategic alliances (r = 0.47, p = 0.002). The regression analysis showed that operational efficiency and strategic alliances together explain 40% of the variance in R&D intensity (R² = 0.40, F(2, 227) = 19.25, p = 0.000). Multivariate regression results confirmed that both operational efficiency (B = 0.07, β = 0.42, p = 0.001) and strategic alliances (B = 1.10, β = 0.35, p = 0.000) are significant predictors of R&D intensity. The study concludes that operational efficiency and strategic alliances significantly enhance R&D intensity in medium-sized companies. These findings suggest that improving operational processes and fostering strategic partnerships are critical for increasing a firm's investment in research and development. The results are consistent with previous research and provide valuable insights for both academia and industry practitioners. Future research should consider longitudinal designs and explore additional variables to further understand these relationships.

The objective of this study was to examine the relationships between entrepreneurial self-efficacy, technological capability, and innovation capability among startup leaders. Specifically, the study aimed to determine the extent to which technological and innovation capabilities predict entrepreneurial self-efficacy, providing insights into the factors that drive entrepreneurial success in startup environments. A cross-sectional research design was employed, involving 290 startup leaders selected based on the sample size recommendations from the Morgan and Krejcie table. Participants completed a structured questionnaire assessing entrepreneurial self-efficacy, technological capability, and innovation capability. Data analysis included descriptive statistics, Pearson correlation, and multiple linear regression using SPSS version 27. Assumptions of linearity, normality, and homoscedasticity were checked and confirmed to ensure the validity of the regression model. Pearson correlation analysis showed significant positive relationships between entrepreneurial self-efficacy and both technological capability (r = 0.52, p < 0.001) and innovation capability (r = 0.56, p < 0.001). Multiple linear regression analysis revealed that technological capability (B = 0.37, SE = 0.08, β = 0.30, t = 4.63, p < 0.001) and innovation capability (B = 0.45, SE = 0.09, β = 0.35, t = 5.14, p < 0.001) were significant predictors of entrepreneurial self-efficacy, explaining 40% of its variance (R^2 = 0.40, F(2, 287) = 46.83, p < 0.001). The study concludes that both technological capability and innovation capability significantly enhance entrepreneurial self-efficacy among startup leaders. These findings underscore the importance of fostering these capabilities to drive entrepreneurial success. The study contributes to the literature by highlighting the critical role of self-efficacy and providing practical implications for entrepreneurs, policymakers, and educators aiming to support entrepreneurial development.

The objective of this study is to understand the customer journey in technology product adoption by exploring the experiences and perspectives of individuals who have recently adopted new technology products. This research aims to identify key themes and factors influencing the customer journey from initial awareness to ongoing engagement and retention, providing insights to enhance customer experiences and inform marketing strategies. This qualitative study employed semi-structured interviews with 20 participants selected through purposive sampling to ensure diverse representation. Participants included individuals who had recently adopted technology products within the past six months. Data collection continued until theoretical saturation was reached. The interview transcripts were analyzed using NVivo software, following an inductive approach to identify patterns and themes. The analysis revealed three main themes: Awareness and Decision-Making, First-Time Use and Learning, and Ongoing Engagement and Retention. Initial awareness was driven by advertising, social media, and word-of-mouth, while decision-making was influenced by peer recommendations, brand reputation, and price sensitivity. The first-time use phase highlighted the importance of intuitive design and support resources. Regular updates, community engagement, and product reliability emerged as critical factors in ongoing engagement and retention. Participants valued seamless omnichannel experiences, personalized marketing efforts, and robust support systems. The study underscores the significance of personalized, seamless customer journeys in technology product adoption. Businesses should focus on integrating physical and digital touchpoints, leveraging AI for personalized experiences, and maintaining active user communities to enhance customer satisfaction and loyalty. Addressing cultural nuances in global markets and managing digital signals effectively can further optimize the customer journey. These insights provide valuable guidance for businesses aiming to improve customer experiences and foster long-term engagement in the competitive technology market.

This study aims to explore the adoption and implementation of emerging technologies in Small and Medium-sized Enterprises (SMEs). By understanding the motivations, challenges, benefits, and future plans of SME founders, this research provides insights into how these businesses leverage technology to enhance their competitiveness and operational efficiency. The study employed a qualitative research design, utilizing semi-structured interviews with 25 founders of SMEs across various industries. Participants were selected using purposive sampling to ensure diverse representation. Data collection continued until theoretical saturation was achieved. The interview data were transcribed and analyzed using NVivo software, following a thematic approach to identify key themes and insights related to technology adoption and implementation in SMEs. The study identified several motivations for technology adoption, including competitive advantage, cost efficiency, customer demand, innovation drive, and growth opportunities. Key challenges faced by SMEs included financial constraints, technical difficulties, resistance to change, regulatory and compliance issues, knowledge gaps, vendor dependence, and time constraints. The benefits realized from technology adoption encompassed improved operational efficiency, enhanced customer satisfaction, revenue growth, better data analytics, increased employee productivity, and greater market adaptability. Future plans of SMEs included continued investment in technology, scaling up implementation, focusing on employee training and development, forming strategic partnerships, enhancing cybersecurity measures, and improving customer engagement. The adoption and implementation of emerging technologies present significant opportunities for SMEs to enhance their competitiveness and operational efficiency. However, SMEs must navigate various challenges to realize these benefits. By adopting a strategic approach and leveraging external partnerships, SMEs can successfully implement new technologies and drive sustainable growth. This study provides valuable insights into the experiences of SME founders, informing both practice and policy in the context of technological advancement in small and medium-sized enterprises.