Global Factors Research Articles

Strategies for the Enhancement of Secondary Metabolite Production via Biosynthesis Gene Cluster Regulation in Aspergillus oryzae

The filamentous fungus Aspergillus oryzae (A. oryzae) has been extensively used for the biosynthesis of numerous secondary metabolites with significant applications in agriculture and food and medical industries, among others. However, the identification and functional prediction of metabolites through genome mining in A. oryzae are hindered by the complex regulatory mechanisms of secondary metabolite biosynthesis and the inactivity of most of the biosynthetic gene clusters involved. The global regulatory factors, pathway-specific regulatory factors, epigenetics, and environmental signals significantly impact the production of secondary metabolites, indicating that appropriate gene-level modulations are expected to promote the biosynthesis of secondary metabolites in A. oryzae. This review mainly focuses on illuminating the molecular regulatory mechanisms for the activation of potentially unexpressed pathways, possibly revealing the effects of transcriptional, epigenetic, and environmental signal regulation. By gaining a comprehensive understanding of the regulatory mechanisms of secondary metabolite biosynthesis, strategies can be developed to enhance the production and utilization of these metabolites, and potential functions can be fully exploited.

An effective segmentation and attention-based reptile residual capsule auto encoder for pest classification.

Insect pests are a major global factor affecting agricultural crop productivity and quality. Rapid and precise insect pest detection is crucial for improving handling and prediction techniques. There are several methods for pest detection and classification tasks; still, the inaccurate detection, computation complexity and several other challenges affect the performance of the model. Thus, this research presents a Deep Learning (DL) approach that has led to significant advancements and is currently being applied successfully in many domains, such as autonomous insect pest detection. Initially, the input images are gathered from the test dataset. The next step in pre-processing the input images is to improve the model capacity by removing unwanted data using the Enhanced Kuan filter method. Then, the pre-processed images are segmented using the Attention-based U-Net method. Finally, a novel Attention Based Reptile Residual Capsule Auto Encoder (ARRCAE) technique is proposed to classify and recognize crop pests. Furthermore, the Improved Reptile Search Optimisation (IRSO) algorithm is employed to fine-tune the classification parameters optimally. As a result, the proposed study enhances performance by classifying crop pest detection systems. The suggested method makes use of a Python tool for simulation, and pest datasets are utilized for result analysis. The suggested model beats other current models with an accuracy of 98%, precision of 97%, recall of 96%, and specificity of 99% for the pest dataset, per the simulation results that were obtained. © 2024 Society of Chemical Industry.

Understanding dollar dynamics and SME performance in GCC economies

PurposeThis study investigates the impact of US dollar volatility and oil rents on the performance of small and medium-sized enterprises (SMEs) in the Gulf Cooperation Council (GCC) region, with an emphasis on understanding how these factors influence SME financing constraints in economies with fixed currency regimes.Design/methodology/approachEmploying a random effects panel regression analysis, this research considers US dollar volatility and oil rents as independent variables, with SME performance, measured through the financing gap, as the dependent variable. Controls such as trade balance, inflation deltas and gross domestic product (GDP) growth are included to isolate their effects on SME financing constraints.FindingsThe study reveals a significant positive relationship between dollar volatility and the financing gap, suggesting that increased volatility can exacerbate SME financing constraints. Conversely, oil rents did not show a significant direct influence on SME performance. The trade balance and inflation deltas were found to have significant effects, highlighting the multifaceted nature of economic variables affecting SMEs.Research limitations/implicationsThe study acknowledges potential biases due to omitted variables and the limitations inherent in the use of secondary data.Practical implicationsFindings offer pertinent guidance for SMEs and policymakers in the GCC region seeking to develop strategies that mitigate the impact of currency volatility and support SME financing.Originality/valueThe research provides new insights into the dynamics of SME performance within fixed currency regimes, which significantly contributes to the limited literature in this area. The paper further underscores the complex connections between global economic factors and SME financial health.

Exploring the Relationship between Solar Panel Adoption and Socio-Economic Factors: A Case Study of Urban Households in Developing Countries

This article will discuss the results of research on how the use of solar panels as an alternative energy source is related to social and economic factors, especially in urban areas in developing countries. This study will analyze factors such as education level, income, and people's perceptions of alternative energy to identify correlations between solar panel use and socio-economic variables. It is hoped that this article can provide new insights for public policy in promoting the use of renewable energy and creating a more inclusive society. This research can be categorized as quantitative. The research method used is the associative research method. The data analysis method used is correlation analysis. The sampling technique used is purposive sampling. The results of the study show that there are different characteristics of the relationship between the use of solar panels and socio-economic variables. Research on the relationship between global solar panel use and factors such as the number of prosumers, total kwh exports, GRDP, total investment, total load, home charging ev, distance to the capital city, air quality, and per capita expenditure has very important uses for stakeholders, especially in terms of sustainable energy development and reducing negative impacts on the environment. Policymakers can use the results of this research to formulate more appropriate policies to promote the use of solar energy and drive the shift to renewable energy sources.

Number of global change factors alters plant-soil feedbacks via its effect on soil fungal communities

Plants and soil microbes are often challenged by multiple global change factors (GCFs), and their responses to single GCFs may differ from those to multiple GCFs due to synergistic or antagonistic interactions. Therefore, changes in number of GCFs may alter plant-soil feedbacks. We conditioned a soil by growing a plant of Hydrocotyle verticillata in the soil for three months, and then grew a plant of the same species in a sterilized bulk soil inoculated with the live or the sterilized conditioned soil under no, single, two or four GCFs. The strength of plant-soil feedbacks calculated by root biomass was positive in the treatment with four GCFs, but not in any of the other three treatments. The strength of plant-soil feedbacks based on shoot biomass was positive in the treatment with single and four GCFs, but not in the treatments with no or two GCFs. Furthermore, the strength of plant-soil feedbacks was closely related to changes in soil fungal composition mediated by changes in number of GCFs. These results indicate that increasing number of GCFs can alter plant-soil feedbacks via changing fungal legacies that plants leave in the soil. Our results highlight the importance of multiple GCFs in regulating plant-microbial interactions.

Revisiting the countercyclicality of fiscal policy

AbstractThis paper provides a novel dataset of time-varying measures on the degree of countercyclicality of fiscal policies for advanced and developing economies between 1980 and 2021. The use of time-varying measures of fiscal stabilization, with special attention to potential endogeneity issues, overcomes the major limitation of previous studies and allows the analysis to account for both country-specific as well as global factors. The paper also examines the key determinants of countercyclicality of fiscal policy with a focus on factors as severe crises, informality, financial development and governance. Empirical results show that (i) fiscal policy tends to be more countercyclical during severe crises than typical recessions, especially for advanced economies; (ii) fiscal countercyclicality has increased over time for many economies over the last two decades; (iii) discretionary and automatic countercyclicality are both strong in advanced economies but acyclical (at times procyclical) in low-income countries; (iv) fiscal countercyclicality operates primarily through the expenditure channel, particularly for social benefits; and (v) better financial development, larger government size and stronger institutional quality are associated with larger countercyclical effects of fiscal policy. Our results are robust to various specifications and endogeneity checks.

Volatility Spillovers among Sovereign Credit Default Swaps of Emerging Economies and Their Determinants

This paper aims to investigate the volatility spillovers among selected emerging economies’ sovereign credit default swaps (SCDSs), including those of Saudi Arabia, Russia, China, Indonesia, South Africa, Brazil, Mexico, and Turkey. Using data from January 2010 to July 2023, we apply the time-domain and the frequency-domain connectedness approaches.Empirical results show that (i) Indonesia, followed by China and Mexico, are the main transmitters of sovereign credit risk volatility. (ii) Among global factors, the volatility index (VIX), economic policy uncertainty (EPU), and global political risk (GPR) positively impacted spillover on lower and higher quantiles. The results offer critical insights for international investors, policymakers, and researchers, emphasizing the importance of risk-aware investment strategies and cautious policy formulation in the context of financial crises and political events.

On the timing of the opening of Western Pacific's back-arc seas: Data from Sr, O and C isotopes in Miocene molluscs of Sakhalin and Hokkaido

This study presents Sr-isotope data to determine the stage affiliation of different parts of the Miocene Sertunaiskaya and Kurasiyskaya formations in Sakhalin, as well as the upper part of the Togeshita Formation in Hokkaido. Additionally, it confirms a diachronous boundary between the Sertunaiskaya and Kurasiyskaya formations. The data received will be useful for globally correlating the Miocene formations of the Sakhalin-Hokkaido area. The study indicates that the Japan and Kuril back-arc basins in the Western Pacific underwent their final opening stages during the Middle Miocene (18.1–17.7 and 15.9–14.2 Ma, respectively), as determined by the SIS method. This process was likely influenced by both global and local factors. One of the local influences on the formation of back-arc basins in the Western Pacific appears to be the influx of cool boreal waters, as indicated by the δ18O signals. The Miocene data from the Sakhalin-Hokkaido area, including evidence from Sr, O, and C isotopes, combined with corresponding paleontological, stratigraphic, palaeomagnetic, and palaeobiogeographic data from the Paleogene and Neogene, provide a four-stage chronological sequence of events related to the opening of the Western Pacific's back-arc seas. This paper presents original information on the Miocene C-isotope excursion in the Western Pacific, which are correlated with the Monterey Event. Recent data on this topic may have enhanced our comprehension of the conditions required for the formation of hydrocarbons, which are found in substantial amounts in the Miocene of northern Sakhalin and Hokkaido.

Interest rates across the world: Global, regional, and idiosyncratic factors

This paper employs a dynamic factor model with endogenous regional clustering and extreme value adjustment properties to construct a world interest rate, as well as regional factors, based on country-specific short-run real interest rates from 43 markets. We find that global and regional factors play crucial roles in determining local rates among advanced countries, while local factors are more important among emerging markets. Further, convenience yields essentially affect both global and regional rates, especially in the longer run. Moreover, the relationship between global and local rates depends crucially on capital account openness, while the choice of exchange rate regime is a critical determinant of the transmission of regional factors. Lastly, we show that a U.S. nominal rate shock would raise the global real rate a quarter later, with a stronger impact observed before the U.S. rate hits the zero lower bound in 2008.

Global burden of bladder cancer mortality in 2020 and 2040 according to GLOBOCAN estimates

IntroductionIn 2020, bladder cancer (BC) was the seventh most prevalent cancer in the world, with 5-year prevalence of more than 1.7 million cases. Due to the main risk factors—smoking and chemical exposures—associated with BC, it is considered a largely preventable and avoidable cancer. An overview of BC mortality can allow an insight not only into the prevalence of global risk factors, but also into the varying efficiency of healthcare systems worldwide. For this purpose, this study analyzes the national mortality estimates for 2020 and projected future trends up to 2040.Materials and methodsAge-standardized mortality rates per 100,000 person-years of BC for 185 countries by sex were obtained from the GLOBOCAN 2020 database, operated by the International Agency for Research on Cancer (IARC). Mortality rates were stratified according to sex and Human Development Index (HDI). BC deaths were projected up to 2040 on the basis of demographic changes, alongside different scenarios of annually increasing, stable or decreasing mortality rates from the baseline year of 2020.ResultsIn 2020, nearly three times more men died from BC than women, with more than 210,000 deaths in both sexes combined, worldwide. Regardless of gender, more than half of the total BC deaths were from countries with a very high HDI. According to our projections, while the number of deaths for men can only increase up to 54% (from 159 to around 163–245 thousand), for women it is projected to increase two- to three-fold (from 50 to around 119–176 thousand) by 2040. The burden of BC mortality in countries with a very high HDI versus high HDI appears to converge by 2040 for both sexes.ConclusionOpposite mortality trends by gender highlight the urgent need for immediate interventions to expand anti-tobacco strategies, especially for women. The implementation of more strict occupational health and safety regulations could also prevent exposures associated with BC. Improving the ability to detect BC earlier and access to treatment can have a significant positive impact on reducing mortality rates, minimizing economic costs, and enhancing the quality of life for patients.

Regimes of global and national oil palm cultivations from 2001 to 2018

Oil palm is the dominant global oil crop due to its high productivity and diversified usage in many sectors. Since the late 20th century, oil palm cultivations proliferated in Southeast Asia, West Africa, and Latin America. However, global market factors, different national and regional policies, and smallholder versus commercial planting regimes in different areas lead to significant differences in economic benefits and environmental problems from deforestation and loss of biodiversity. We investigated changes in global and national distributions against suitability and indices of tree age distribution change for industrial oil palm (IOP) and smallholder oil palm (SOP). Spatial and temporal change analyses show that: i) For most tree ages, the proportion of global oil palm planting in suitable areas was less than 50%, but the impact of temporal regimes, from possible market factors and local policies, on planting structure should not be neglected; ii) Central America, South America, and West Africa were less suitable for oil palm cultivation compared to Southeast Asia. While, as two dominant oil palm planting countries, Indonesia and Malaysia had relatively low planting suitability, with 39.23% of Indonesia’s IOP, 44.85% of Indonesia’s SOP, 30.90% of Malaysia’s IOP and 18.77% of Malaysia’s SOP in highly and most suitable intervals; iii) There exist clear differences between IOP and SOP in terms of suitability, planting structure and spatial expansion patterns; iv) Hysteresis effect exists between latecomers (countries in West Africa and Latin America) and forerunner (Indonesia and Malaysia) in terms of spatial expansion; v) The spatial expansion patterns of oil palm planting centers have obvious scale effects for both IOP and SOP, with clear inter-country and intra-country differences. This paper reinterprets the global distribution of tree age and spatial expansion pattern and recommends scientific strategies to guide site selection and planting structure that enable oil palm cultivation for sustainable development.

Strategic Priorities for the Development of Food Security of the State Taking into Account Hybrid Threats and Challenges

Relevance. Food security is one of the most urgent and complex problems of the modern world. In the context of globalization, climate change, economic fluctuations and other factors, ensuring the stability and reliability of food security is becoming a complex task that requires effective strategies.The purpose is to analyze and evaluate strategic priorities in the field of food security, taking into account complex hybrid threats and challenges. Objectives: identification of strategic alternatives; development of a mathematical model with fuzzy variables and rules for evaluating strategic alternatives; analysis of evaluation results to identify the most effective strategic directions.Methodology. The research methodology included the use of fuzzy logic methods to build a mathematical model for evaluating strategic alternatives.Results. As a result of the study, it was revealed that the strategic alternative S1 received the highest rating, emphasizing its key importance for the sustainability of the food system. Alternatives S7, S9, S4 and S3 also have high ratings, which makes them potentially effective for ensuring food security. Alternatives S10, S8, S2, S6 and S5 are less of a priority, but are important for reducing various risks associated with food security, such as lack of food and vulnerability of supplies.Conclusions. The results of the study allowed us to conclude that the use of the fuzzy logic tool provides an opportunity to conduct a deeper and more comprehensive analysis of complex factors affecting food security. This approach allows us to take into account the uncertainty and multidimensionality of data related to the food system and introduce more accurate modeling of this dynamic area. Such a method enriches the decision-making process in the field of food security, contributing to the development of more effective strategic decisions and management approaches that take into account a variety of factors and their interaction. This contributes to improving the quality of research and helps to form more rational and informed strategies that contribute to ensuring the stability and reliability of food security.

The global burden and risk factors of cardiovascular diseases in adolescent and young adults, 1990–2019

BackgroundTo provide details of the burden and the trend of the cardiovascular disease (CVD) and its risk factors in adolescent and young adults.MethodsAge-standardized rates (ASRs) of incidence, mortality and Disability-Adjusted Life Years (DALYs) were used to describe the burden of CVD in adolescents and young adults. Estimated Annual Percentage Changes (EAPCs) of ASRs were used to describe the trend from 1990 to 2019. Risk factors were calculated by Population Attributable Fractions (PAFs).ResultsIn 2019, the age-standardized incidence rate (ASIR), age-standardized mortality rate (ASMR) and age-standardized DALYs rate (ASDR) of CVD were 129.85 per 100 000 (95% Confidence interval (CI): 102.60, 160.31), 15.12 per 100 000 (95% CI: 13.89, 16.48) and 990.64 per 100 000 (95% CI: 911.06, 1076.46). The highest ASRs were seen in low sociodemographic index (SDI) and low-middle SDI regions. The burden was heavier in male and individuals aged 35–39. From 1990 to 2019, 72 (35.29%) countries showed an increasing trend of ASIR and more than 80% countries showed a downward trend in ASMR and ASDR. Rheumatic heart disease had the highest ASIR and Ischemic Heart Disease was the highest in both ASMR and ASDR. The main attributable risk factor for death and DALYs were high systolic blood pressure, high body-mass index and high LDL cholesterol.ConclusionsThe burden of CVD in adolescent and young adults is a significant global health challenge. It is crucial to take into account the disparities in SDI levels among countries, gender and age characteristics of the population, primary types of CVD, and the attributable risk factors when formulating and implementing prevention strategies.

Market transformations in the development of land relations (experience of Canada and Scotland)

The development of land relations today remains one of the most important tasks of Ukraine. The priority directions for the development of such relations are the creation of conditions for the inclusion of land to the economic circulation and the regulation of land relations in combination with the increase of budget revenues of all levels from the use of land.
 In modern conditions, the land policy of developed countries creates mechanisms for regulating the land market with a multifaceted socio-economic content. This complex process is accompanied by the reform of land relations. Effective management of land resources ensures the development of the land market. Discussions regarding the creation of a full-fledged land market in Ukraine are still ongoing. Analysis of foreign experience will allow to transform land relations in Ukraine.
 Natural resources are diverse, but land is becoming an increasingly valuable resource for most countries. They are trying to achieve high rates of economic growth, which, in turn, are related to the development of land resources. It often turns out that such «development» is connected with the seizure of a million hectares of agricultural land by rich corporations or even foreign states. Deprivation of rights to land claims the status of the most famous and, at the same time, little-researched form of deprivation of rights not only in modern literature devoted to «land grabbing», but also in classical theory in general. The article discusses the factors contributing to «land grabbing». Global factors causing similar processes in Canada and Scotland, current consequences, future risks are identified. It was established that the factors of «grabbing of land» for agricultural purposes are of different nature. This is a significant price differentiation on the land market, and the increase in the investment attractiveness of agricultural land, the consequences of privatization and land consolidation. The leading role of the state in the investment process and the existence of a strategy for spreading the influence of the countries in question, which is implemented in parallel with the purchase of land, are shown.

Heterogeneous Fusion and Integrity Learning Network for RGB-D Salient Object Detection

While significant progress has been made in recent years in the field of salient object detection (SOD), there are still limitations in heterogeneous modality fusion and salient feature integrity learning. The former is primarily attributed to a paucity of attention from researchers to the fusion of cross-scale information between different modalities during processing multi-modal heterogeneous data, coupled with an absence of methods for adaptive control of their respective contributions. The latter constraint stems from the shortcomings in existing approaches concerning the prediction of salient region’s integrity. To address these problems, we propose a Heterogeneous Fusion and Integrity Learning Network for RGB-D Salient Object Detection, denoted as HFIL-Net. In response to the first challenge, we design an Advanced Semantic Guidance Aggregation (ASGA) module, which utilizes three fusion blocks to achieve the aggregation of three types of information: within-scale cross-modal, within-modal cross-scale, and cross-modal cross-scale. In addition, we embed the local fusion factor matrices in the ASGA module and utilize the global fusion factor matrices in the Multi-modal Information Adaptive Fusion (MIAF) module to control the contributions adaptively from different perspectives during the fusion process. For the second issue, we introduce the Feature Integrity Learning and Refinement (FILR) Module. It leverages the idea of ”part-whole” relationships from capsule networks to learn feature integrity and further refine the learned features through attention mechanisms. Extensive experimental results demonstrate that our proposed HFIL-Net outperforms over 17 state-of-the-art (SOTA) detection methods in testing across seven challenging standard datasets. Codes and results are available on https://github.com/BojueGao/HFIL-Net.

Domestic and Global Causes for Exchange Rate Volatility: Evidence From Turkey

Understanding which factors influence exchange rate movements is important for understanding economic development, trade patterns, investment decisions, and for designing economic policies. In this study, we allow for monetary, domestic and global financial variables to assess the relevant importance of each of the variables to exchange rate volatility in the case of Turkey. The paper investigates the dynamics of exchange rate volatility of the Turkish lira in a complementary perspective by employing both Generalized autoregressive conditional heteroskedasticity (GARCH) method and Lyapunov exponents over the period from March 1, 2019 to November 11, 2021. Firstly, decomposing the impact of domestic and global financial variables, the results of the GARCH model indicate that the exchange rate volatility is affected by Volatility index (VIX) and Credit default swaps (CDS). This result suggests that the exchange rate shocks experienced are mainly caused by global factors, therefore policymakers should focus on volatility spillovers caused by global financial markets. Secondly, detected positive maximal Lyapunov exponent shows that complexity in foreign exchange markets has been increased, market expectations lead to multiple-equilibria and diverging volatility eventually will generate recurrent spikes in currency value. These complementary findings have important implications for interventions of Central banks and preventing systemic risks, as well as portfolio and risk management. JEL Classification: C32, G14, G15.

Warming and increased precipitation alter soil carbon cycling in a temperate desert steppe of Inner Mongolia

Abstract Warming and precipitation are key global change factors driving soil carbon (C) dynamics in terrestrial ecosystems. However, the effects of warming and altered precipitation on soil microbial diversity and functional genes involved in soil C cycling remain largely unknown. We investigated the effects of warming and increased precipitation on soil C cycling in a temperate desert steppe of Inner Mongolia using metagenomic sequencing. We found that warming reduced plant richness, Shannon–Wiener and Simpson index. In contrast, increased precipitation significantly influenced Shannon–Wiener and Simpson index. Warming reduced soil microbial species by 5.4% while increased precipitation and warming combined with increased precipitation led to increases in soil microbial species by 23.3% and 2.7%, respectively. The relative abundance of Proteobacteria, which involve C cycling genes, was significantly increased by warming and increased precipitation. Warming significantly reduced the abundance of GAPDH (Calvin cycle) and celF (cellulose degradation) while it enhanced the abundance of glxR (lignin degradation). Increased precipitation significantly enhanced the abundance of pgk (Calvin cycle), coxL (carbon monoxide oxidation), malZ (starch degradation), and mttB (methane production). Moreover, a wide range of correlations among soil properties and C cycling functional genes was detected, suggesting the synergistic and/or antagonistic relationships under scenario of global change. These results may suggest that warming is beneficial to soil C storage while increased precipitation negatively affects soil C sequestration. These findings provide a new perspective for understanding the response of microbial communities to warming and increased precipitation in the temperate desert steppe.

A New Measure of Climate Transition Risk Based on Distance to a Global Emission Factor Frontier

Targeted financing of transition to a "net zero" global economy entails climate transition risk. We propose a measure of transition risk at the country-sector dyad level composed of five tiers of transition risk based on two factors: i) the gap between a dyad's existing emission factor (EF) – a measure of the greenhouse gas intensity of output – and the global 'frontier' sectoral EF, and ii) a dyad's recent convergence towards the frontier EF. Dyads that are either close to the frontier or converging towards the frontier carry lower transition risk. Our measure, using 45 sectors across 66 countries, accounts for both direct greenhouse gas emissions as well as those that enter into production through complex supply chains as captured by intercountry, input-output tables, and can be applied at different levels of stringency to high-, middle-, and low-income economies. Our measure thus accounts for, and sheds light on, EF reductions through investment in lower emissions production techniques in own facilities as well as sourcing intermediate inputs with lower embodied emissions.

Multiple global change factors cause declines of a temperate bryophyte

ABSTRACT Background Climate change, nutrient enrichment and land use have been predicted to alter bryophyte abundance and performance; we expect these factors to interact, yet experiments addressing their joint effects are missing. Aims We tested the responses of Brachythecium rutabulum, a common temperate moss species, to single and combined effects of future climatic conditions, grazing, light limitation and nutrient enrichment. We predicted that future climatic conditions, intense grazing, light limitation and nutrient enrichment have all negative effects on the survival and photosynthetic condition of B. rutabulum, and their joint effects become strong even if individual factors have only weak effects (multiple stress hypothesis). Methods We measured after two growing seasons biomass and chlorophyll fluorescence of transplanted moss colonies in full-factorial treatments of fertilisation, exclusion of sheep grazing and light amendment by LED lamps, replicated in ambient and future climatic conditions. Results Future climate and fertilisation had negligible effects on colony biomass and chlorophyll fluorescence of bryophyte colonies, whereas light amendment had positive effect on chlorophyll fluorescence and grazing exclusion had positive effect on colony biomass. Colony biomass and chlorophyll fluorescence decreased with increasing number of global change factors. Conclusion Supporting the multiple stress hypothesis, individually weak global change factors can combine to strong joint effects.

An Integrated Machine Learning Approach for Congestive Heart Failure Prediction.

Congestive heart failure (CHF) is one of the primary sources of mortality and morbidity among the global population. Over 26 million individuals globally are affected by heart disease, and its prevalence is rising by 2% yearly. With advances in healthcare technologies, if we predict CHF in the early stages, one of the leading global mortality factors can be reduced. Therefore, the main objective of this study is to use machine learning applications to enhance the diagnosis of CHF and to reduce the cost of diagnosis by employing minimum features to forecast the possibility of a CHF occurring. We employ a deep neural network (DNN) classifier for CHF classification and compare the performance of DNN with various machine learning classifiers. In this research, we use a very challenging dataset, called the Cardiovascular Health Study (CHS) dataset, and a unique pre-processing technique by integrating C4.5 and K-nearest neighbor (KNN). While the C4.5 technique is used to find significant features and remove the outlier data from the dataset, the KNN algorithm is employed for missing data imputation. For classification, we compare six state-of-the-art machine learning (ML) algorithms (KNN, logistic regression (LR), naive Bayes (NB), random forest (RF), support vector machine (SVM), and decision tree (DT)) with DNN. To evaluate the performance, we use seven statistical measurements (i.e., accuracy, specificity, sensitivity, F1-score, precision, Matthew's correlation coefficient, and false positive rate). Overall, our results reflect our proposed integrated approach, which outperformed other machine learning algorithms in terms of CHF prediction, reducing patient expenses by reducing the number of medical tests. The proposed model obtained 97.03% F1-score, 95.30% accuracy, 96.49% sensitivity, and 97.58% precision.