Exploratory Data Analysis Techniques Research Articles

Predictors of the willingness to accept a free COVID-19 vaccine among households in Nigeria

BackgroundTo inform vaccination policy and programmatic strategies to increase COVID-19 vaccine uptake, an understanding of the factors associated with the willingness to vaccinate is needed. MethodsWe analyzed data collected from the sixth and tenth round of the Nigerian COVID-19 National Longitudinal Phone Survey conducted by the World Bank in 2020 and 2021, respectively. Exploratory data analysis and feature selection techniques were used to identify important variables. Multivariable logistic regression models were fitted to assess the association between socio-demographic and economic factors and the willingness to receive a free COVID-19 vaccine among Nigerian households at two different time points before vaccines became widely available. ResultsData from 1733 and 1651 Nigerian households who completed the sixth and tenth round of the survey, respectively, were included. Most respondents (>85 % of households) were willing to receive a free COVID-19 vaccine from both survey rounds. The median household size was 6 (IQR: [4, 8]) with females heading about 18 % of the households. Approximately 22 % of the household heads had not received any formal education. Compared to households whose head had no education, households whose heads had completed tertiary education or higher had significantly lower odds of willingness to be vaccinated (ORround 6: 0.46, 95 % CI: [0.31, 0.68], ORround 10: 0.49, 95 % CI: [0.34, 0.71]). An increasing proportion of male household members was associated with greater willingness to receive a free COVID-19 vaccine (ORround 6: 1.84, 95 % CI: [1.01, 3.33], ORround 10: 5.25, 95 % CI: [2.86, 9.65]). Significant associations with vaccine willingness were also observed across geopolitical zones of residence with households in South-East Nigeria (ORround 6: 0.16, 95 % CI: [0.10, 0.24]; ORround 10: 0.29, 95 % CI: [0.19, 0.43]) and South-South Nigeria (ORround 6: 0.57, 95 % CI: [0.36, 0.90], ORround 10: 0.32, 95 % CI: [0.22, 0.48]) less likely to be willing to receive a free vaccine compared to households in North-Central Nigeria. ConclusionThese findings from two different time points before vaccine roll-out suggest that the educational level of household head, proportion of male household members, and the geopolitical zone of residence are important baseline predictors of the willingness to receive a free COVID-19 vaccine in Nigeria. These factors should be carefully considered and specifically targeted when designing public health programs to inform early-stage strategies that address underlying vaccine hesitancy, improve vaccine uptake, promote ongoing COVID-19 vaccination efforts, and potentially enhance other immunization programs in Nigeria.

Multiclass Text Classification of Indonesian Short Message Service (SMS) Spam using Deep Learning Method and Easy Data Augmentation

The ease of using Short Message Service (SMS) has brought the issue of SMS spam, characterized by unsolicited and unwanted. Many studies have been conducted utilizing machine learning methods to build models capable of classifying SMS Spam to overcome this problem. However, most of these studies still rely on traditional methods, with limited exploration of deep learning-based approaches. Whereas traditional methods have a limitation compared to deep learning, which performs manual feature extraction. Moreover, many of these studies only focus on binary classification rather than multiclass SMS classification which can provide more detailed classification results. The aim of this research is to analyze deep learning model for multiclass Indonesian SMS spam classification with six categories and to assess the effectiveness of the text augmentation method in addressing data imbalace issues arising from the increased number of SMS categories. The research method used were Indonesian version of Bidirectional Encoder Representations from Transformers (IndoBERT) model and exploratory data analysis (EDA) augmentation technique to address imbalance dataset issue. The evaluation is conducted by comparing the performance of the IndoBERT model on the dataset and applying EDA techniques to enhance the representation of minority classes. The result of this research shows that IndoBERT achieves 91% accuracy rate in classifying SMS spam. Furthermore, the use of EDA technique results in significant improvement in f1-score, with an average 12% increase in minority classes. Overall model accuracy also improves to 93% after EDA implementation. This research concludes that IndoBERT is effective for multiclass SMS spam classification, and the EDA is beneficial in handling imbalanced data, contributing to the enhancement of model performances.

The evaluation and optimization of the agricultural sustainable development based on a data-driven approach: A case from Northern Anhui

The sustainable advancement of agriculture stands as the fundamental cornerstone of sustainable human progress. This study introduces a data-centric methodological framework founded upon the holistic delineation of measurement, feature assessment, and pathway enhancement for agricultural sustainability. Initially, the research articulates a comprehensive evaluative schema incorporating sub-dimensions encompassing agricultural production, agricultural economics, the agricultural resource environment, and rural society, grounded in sustainable development theory. Subsequently, it devises a methodological apparatus for assessing and enhancing sustainable development capabilities, employing entropy evaluation methods and exploratory spatial data analysis techniques. Employing North Anhui as a case study, the viability of this approach is substantiated. The empirical inquiry conducted within this article operationalizes comprehensive evaluation and explores pathways for optimizing agricultural sustainability, focusing on the period spanning 2011 to 2020 in Northern Anhui. The findings affirm the feasibility and efficacy of the data-driven approach. Recommendations derived from the empirical exploration of agricultural sustainability pathways at the local level offer valuable insights for governmental authorities and policymakers. This research endeavor could be extrapolated to other geographical locales worldwide, fostering innovative strides in the sustainable development of regional agriculture.

AI Assisted Deceptive Content Analysis System Using Bi-LSTM

Abstract: In an age of widespread misinformation, creating accurate machine learning algorithms is vital for ensuring the integrity of information sharing. This project aims to develop a machine learning model capable of distinguishing between fake and true news articles. [1]The model utilizes a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network architecture. Exploratory data analysis techniques are employed to gain in-sights into the characteristics and distributions within the dataset. Data visualization techniques aid in understanding patterns and relationships within the dataset. Additionally, unigram analysis is conducted to extract meaningful features from the text data. The datasets are then prepared for model training, involving preprocessing steps such as tokenization and vectorization. Fi-nally, the Bi-LSTM model is constructed, leveraging its ability to capture long-range dependencies in sequential data. The model is trained on the prepared datasets, optimized using appropri-ate techniques, and evaluated using metrics such as accuracy, precision, recall, and F1-score. The Bi-LSTM architecture offers the advantage of capturing long-range dependencies in sequential data, thereby enhancing the model’s ability to discern nuanced patterns in news articles. The primary objective of this project is to develop a robust and accurate system for automatically detecting fake news, thus playing a pivotal role in enhancing the dissemination of reliable information in the digital age.

Expediting structure–property analyses using variational autoencoders with regression

We present a machine learning approach that expedites structure–property analysis in materials, bypassing traditional feature extraction and exploratory data analysis techniques. This objective is accomplished by employing a variational autoencoder (VAE) structure that is modified to include a regressor network for property prediction (VAE-Regression). This modification allows for direct linkage of imaged features and quantitative part properties within the VAE latent space. We first demonstrate our approach using 2D optical micrographs and corresponding four-point bend fatigue life data from laser beam powder bed fusion additively manufactured Ti-6Al-4V coupons. The VAE-Regression model extracts spatial features, predicts fatigue life, and identifies features of porosity defect governing fatigue behavior such as pore clusters, pores near sample edges, and jagged pore morphologies. These features corroborate fatigue literature on physics-based modeling and experimentation. We then demonstrate the versatility of our methodology using binder jet additively manufactured WC-Co coupons, where porosity and microstructural discontinuities are known to lower the three-point bend transverse rupture strength, but the interaction between the WC and Co are yet to be completely understood. We attempted to understand these interactions using our VAE-Regression architecture. Within our dataset, we show that coarser WC grains surrounded by larger Co pools indicate lower strength, while finer WC grains with smaller Co pools indicate higher strength. This machine learning approach using image-based data will likely prove to be critical in understanding and identifying structure–property relationships in new materials and manufacturing processes.

Case Study: Performance Analysis throughout the History in Summer and Winter Olympics

The Olympics is the world’s foremost multisport international event that occurs every four years in 2 seasonal editions which are the Summer and Winter Olympics respectively. With more than 200 nations participating in hundreds of events, it is a matter of prestige for any nation and the dream of every sportsperson to win a medal in the Olympics. Despite the massive hard work of the sportspersons, many nations fail to win medals whereas some nations are able to grab most of the medals under the names of their nations. Therefore, an analysis of the history of the Olympics is needed to understand the performances of nations, athletes and events to observe certain patterns occurring throughout the history of Olympics, in order to help the nations to improve themselves. The primary objective of this paper is to analyze the historical data of both the Winter and Summer Olympics under 14 different factors using Exploratory Data Analysis (EDA) techniques and statistical methods to evaluate the performance of nations and athletes as well as the historical evolution of Olympics and correlation of medal win of sports persons with external variables such as GDP and population. The analyses provide accurate insights of the performance of nations in the Olympics throughout history and help sportspersons to analyze their own and the competitor’s performances. In this paper, the data visualization aspect of EDA is used to provide a statistical view of factors that led to the evolution of the Olympic games as well as the performance of nations and sportspersons and helps countries with average and poor performances to improve themselves in upcoming Olympic editions.

SLA-Adaptive Threshold Adjustment for a Kubernetes Horizontal Pod Autoscaler

Kubernetes is an open-source container orchestration system that provides a built-in module for dynamic resource provisioning named the Horizontal Pod Autoscaler (HPA). The HPA identifies the number of resources to be provisioned by calculating the ratio between the current and target utilisation metrics. The target utilisation metric, or threshold, directly impacts how many and how quickly resources will be provisioned. However, the determination of the threshold that would allow satisfying performance-based Service Level Objectives (SLOs) is a long, error-prone, manual process because it is based on the static threshold principle and requires manual configuration. This might result in underprovisioning or overprovisioning, leading to the inadequate allocation of computing resources or SLO violations. Numerous autoscaling solutions have been introduced as alternatives to the HPA to simplify the process. However, the HPA is still the most widely used solution due to its ease of setup, operation, and seamless integration with other Kubernetes functionalities. The present study proposes a method that utilises exploratory data analysis techniques along with moving average smoothing to identify the target utilisation threshold for the HPA. The objective is to ensure that the system functions without exceeding the maximum number of events that result in a violation of the response time defined in the SLO. A prototype was created to adjust the threshold values dynamically, utilising the proposed method. This prototype enables the evaluation and comparison of the proposed method with the HPA, which has the highest threshold set that meets the performance-based SLOs. The results of the experiments proved that the suggested method adjusts the thresholds to the desired service level with a 1–2% accuracy rate and only 4–10% resource overprovisioning, depending on the type of workload.

Investigation of light gas adsorption by microporous materials using data analysis and decision tree machine learning algorithm

This study aimed to extract insights into the adsorption behavior of light gases by microporous materials such as zeolites, MOFs, and activated carbons. Data reported in 22 articles published between 1974 and 2022 were analyzed using a decision tree (DT) machine learning algorithm. A comprehensive database comprising 3297 data points, elucidating the impacts of 8 input variables on adsorption capacity, was constructed. Various exploratory data analysis techniques, including histograms, bar charts, and scatter plots, were employed to discern the relationships among input variables and the performance variable. Additionally, a DT model was employed to regress the adsorbed capacity data. Furthermore, a parametric study of this model facilitated the determination of the relative importance of input variables and their partial dependence, enhancing the interpretability of the model and enabling the deduction of heuristics for high or low adsorption capacity. The exploratory data analysis revealed that pressure and molecular mass of the adsorbate were the most significant variables influencing adsorption capacity.

Marble Powder as a Soil Stabilizer: An Experimental Investigation of the Geotechnical Properties and Unconfined Compressive Strength Analysis.

Fine-grained soils present engineering challenges. Stabilization with marble powder has shown promise for improving engineering properties. Understanding the temporal evolution of Unconfined Compressive Strength (UCS) and geotechnical properties in stabilized soils could aid strength assessment. This study investigates the stabilization of fine-grained clayey soils using waste marble powder as an alternative binder. Laboratory experiments were conducted to evaluate the geotechnical properties of soil-marble powder mixtures, including Atterberg's limits, compaction characteristics, California Bearing Ratio (CBR), Indirect Tensile Strength (ITS), and Unconfined Compressive Strength (UCS). The effects of various factors, such as curing time, molding water content, and composition ratios, on UCS, were analyzed using Exploratory Data Analysis (EDA) techniques, including histograms, box plots, and statistical modeling. The results show that the CBR increased from 10.43 to 22.94% for unsoaked and 4.68 to 12.46% for soaked conditions with 60% marble powder, ITS rose from 100 to 208 kN/m2 with 60-75% marble powder, and UCS rose from 170 to 661 kN/m2 after 28 days of curing, molding water content (optimum at 22.5%), and composition ratios (optimum at 60% marble powder). Complex modeling yielded R2 (0.954) and RMSE (29.82 kN/m2) between predicted and experimental values. This study demonstrates the potential of utilizing waste marble powder as a sustainable and cost-effective binder for soil stabilization, transforming weak soils into viable construction materials.

An investigation of the COVID-19 impact on liver cancer using exploratory and predictive analytics

This study presents the influence of COVID-19 and the pandemic on individuals diagnosed with hepatocellular carcinoma and intrahepatic cholangiocarcinoma, the two most common types of primary liver cancer. The study compares the effects before and after the pandemic on these patients. Additionally, it endeavors to predict the likelihood of survival for liver cancer patients. Our research will employ various methodologies to investigate this. Exploratory data analysis techniques are utilized, including univariate analysis, correlation analysis, bivariate analysis, chi-square testing, and T-sample testing. For predictive analytics, machine learning algorithms such as Logistic Regression, Decision Trees, Classification And Regression Tree (CART), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Support Vector Machines (SVMs) will be applied. For our outputs, Logistic Regression and SVMs emerged as top-performing algorithms, boasting a remarkable accuracy rate of 93%. The study reveals that COVID-19 affected all age groups similarly. However, a gender-based difference was observed, indicating that males faced a higher risk of both cancer and mortality. Furthermore, the study found that variables such as year, month, bleeding, cirrhosis, and previously known cirrhosis did not significantly influence patient survival.

National or Regional Recruitment: “Market Area” of University Cities

The depopulation of eastern Portugal and concentration on the coast has been a longstanding process. Since 1973 new HEIs has been disseminated around the country, and nowadays, every Portuguese region holds a set of HEIs. However, those closer to the coast tend to have a higher demand than those more distant. It is not clear if each one has a kind of restricted market area, such as a spatial monopoly in its neighbouring area or a national recruitment for some of them. This paper is the first exercise of determination of these areas for the Portuguese public network of HEIs. We use a database from the Ministry of Education (DGEEC-Ministry of Education) with information of the pair, place of residence (municipality level) and respective HEI, for enrolled students. Through techniques of Exploratory Spatial Data Analysis (ESDA), we will look for clusters of municipalities that could be market areas for each “university city” considered. We also use fractional regression models that confirm the positive correlation between the chosen university city and the distance to the family home and belonging to the market area. Despite some overlaps, the market areas of the university cities look to be well defined.

Overview and Exploratory Analyses of CICIDS2017 Intrusion Detection Dataset

Intrusion detection systems are used to detect attacks in a network. Machine learning (ML) approaches have been widely used to build such Intrusion Detection Systems (IDSs) because they are more accurate when built from a very large and representative dataset. In recent times, one of the benchmark datasets that are used to build ML-based Intrusion Detection models is CICIDS2017 dataset. The dataset is contained in eight groups and was collected from the Canadian Institute on Cyber Security dataset repository. The dataset is available in both PCAP and netflow formats. This study used the netflow records in the CIDIDS2017 dataset as they are found to contain newer attacks,very large and are found useful for traffic analysis. Exploratory Data Analysis (EDA) techniques were used to reveal various characteristics of the dataset. The general objective is to provide more insights on the nature, structure and issues with the dataset so as to identify the best ways for using it to achieve improved ML-based IDS models. Furthermore, some of the open problems that can arise from the use of the dataset in any machine learning-based Intrusion Detection systems are highlighted and possible solutions are briefly discussed. The EDA techniques used revealed important relationships among input variables and the target class. The study concluded that the EDA can better influence the decision of future IDS researches that use the dataset. Thus, improved machine learning-based intrusion detection systems can be built from the dataset once it is well understood and pre-processed.

Enhancing Educational Assessment: Predicting and Visualizing Student Performance using EDA and Machine Learning Techniques

Abstract— Educational institutions must manually evaluate student's performance, which requires a significant amount of faculty time and effort. Teachers can easily monitor and document student's learning behavior if they utilize a learning management system (LMS). It can be utilized by coaching sessions or educational institutions to quickly analyze student's performance. Due to the enormous number of pupils, huge data must be analyzed; teachers frequently encounter challenges. The qualities of learners who are students are described analytically in this analysis. In this work, student's cognitive and psychomotor skills are predicted and visualized using Exploratory Data Analysis (EDA) and Machine Learning techniques like KNN and Multiple Regressions. The maximum accuracy of 99% has been obtained in Multiple Regression. Keywords—Analytics; Education; Learning Management System; Performance; Students;

Performance Comparison of Decision Tree and Support Vector Machine Algorithms for Heart Failure Prediction

Heart failure is a significant global cause of mortality and morbidity. This research article aims to evaluate the performance of decision tree (DTree) and support vector machine (SVM) methods in predicting heart disease. The study utilizes a dataset with diverse features and employs exploratory data analysis (EDA), clustering, and classification techniques to gain insights and evaluate the performance of the two methods. The results demonstrate the effectiveness of both DTree and SVM in predicting heart disease. Notably, SVM outperforms DTree in terms of accuracy, precision, recall, and F1-score. However, the performance of these methods is influenced by the preprocessing steps applied, indicating the importance of selecting appropriate data preprocessing techniques for optimal performance with specific machine learning algorithms. This study emphasizes the potential of machine learning algorithms in predicting heart disease and underscores the significance of thoughtful preprocessing technique selection to enhance performance.

Study on the historical landscape evolution of the core reserve of Guangfu Ancient City based on computer simulation

Abstract In the conservation of the historical landscape within the Guangfu Ancient City Core Protection Area, traditional descriptions via drawings and textual narratives often exhibit subjective biases. To address this, a theoretical model for the historical architectural landscape has been developed to objectively measure and characterize the spatial patterns and morphologies of the landscape. This study introduces Exploratory Spatial Data Analysis (ESDA) techniques, utilizing spatial weights and spatial correlation measures as primary analytical tools. Additionally, meta-cellular automata are employed to simulate the spatial and temporal evolution of the historical landscape. Geographical Information Systems (GIS) and Remote Sensing (RS) technologies have been leveraged to establish a spatial database and facilitate the visualization of the historical landscape’s evolutionary processes. This integrated model and technological approach enable a detailed analysis of the architectural landscape characteristics and the spatial structure’s evolution within the study area. The findings reveal distinct spatial distribution characteristics, with an undulation index ranging from 5 to 20 and a predominant index over 7, accounting for 15.67% of the land. Based on these insights, the study proposes strategic interventions to safeguard the historical integrity of Guangfu Ancient City’s core conservation area.

An Assessment of the Relationship Between Population Density and the Incidence of Dengue Hemorrhagic Fever

Background: Dengue is a leading cause of severe illness and hospitalization in Vietnam. This study aims to assess the relationship between population density and dengue hemorrhagic fever (DHF) in August 2023 in Ho Chi Minh city, Vietnam. Methods: Exploratory data analysis (EDA) techniques were first applied to investigate the distribution patterns of DHF incidence. It will go on to use Getis Ord statistic to detect hotspots and coldspots of DHF infection rates. A scatter plot will be then employed to quantitatively assess the relationship between population density and DHF infection rates and Getis Ord statistic. Finally, a dataset of DHF cases reported in August 2023 in Ho Chi Minh city was used to illustrate the proposed methods. Results: It was found that high values of Getis Ord coefficients was mainly concentrated in the east and west of the city. There was a negative correlation between population density and both variables (DHF infection rates and Getis Ord statistic). The population density likely decreases dengue hemorrhagic fever infection rates in August in Ho Chi Minh city. Conclusions: It can be concluded that spatial statistics and EDA have proved their effectiveness in the assessment of the relationship between population density and the incidence of dengue hemorrhagic fever. Findings of this study provide an insight into the effects of population density on DHF infection rates. Keywords: Relationship assessment, Population density, Spatial autocorrelation analysis, Dengue hemorrhagic fever, Infection rate, Ho Chi Minh city.

Integrating Adam Optimizer to Enhance Efficiency of Transfer Learning Model for Diagnosing Cancers

Despite huge advancements in medical fields, diseases like cancer continue to plague people since we are still prone to them. The main purpose is to design a critical analysis of cancers, including breast, pancreatic, colon, skin, lung, and other cancers from the year 1990 to 2019, to identify some of the rising factors of both lung and colon cancers that cause unnecessary deaths. Thus, early detection on the other hand greatly improves the chances of survival rates in humans. At that place, Transfer Learning (TL) techniques have made significant improvements these days.
 Material and Methods: We herein implemented the exploratory data analysis (EDA) technique to analyze 29 types of cancer deaths worldwide since 1990th century. Between them, we have emphasized lung cancer (LC) and colon cancers (CC) for our study and taken 25,000 histopathology image data from the publicly available Kaggle repository. Further, we proposed a novel methodology using the EfficientNetB7 model defining each step to classify five types of lung and colon tissues (two benign and three malignant) from the histopathological images. Although, an algorithm has been designed that clearly describes the workflow of our proposed method. This experiment has been done through input_shape=(X, Y, 3) on the ‘ImageNet’ dataset, Adam optimizer, and accuracy metrics using Python 3.8.8 software on Jupyter 6.4.3 environment.
 Results: We evaluated the model progress using 50 epochs during the training phase, which resulted in more than 98% accuracy and less than 2% loss in both the training/ validation phases. Besides that, we designed a classification report that describes the performance of our model, where we achieved more than 98.07% accuracy score, 98% precision score, 98% of recall score, and 98% of f1-score. These results signify that implementing an optimization technique on the EfficientNetB7 model improves the overall performance as compared to 93.19% for LC 1 and 93.91% for CC 2 for the RestNet50 model.
 Conclusion: A novelty of this research specifies the methodology to predict LC and CC by applying the EfficientNetB7 model to the cancer dataset which might be beneficial for both doctors as well as healthcare firms.

Measuring economic resilience of manufacturing organization leveraging integrated data envelopment analysis (DEA)-machine learning approach

ABSTRACT This study aims to assess the economic resilience of manufacturing firms through a combination of output-oriented data envelopment analysis (DEA) and machine learning techniques. The research draws on economic resilience factors identified in the literature and focuses on three categories: economic-related factors (financial flexibility, microeconomic market, macroeconomic stability), production-related factors (restoration of production, backup inventories, resource pooling/sharing), and management-related factors (diversification of activities, good governance (management), relocation). Using DEA, a mathematical approach, the study computes and analyzes the contributions of various components to economic resilience. The results of DEA normalization indicate that the highest weighted criteria are financial flexibility, good governance (management), and resource pooling (sharing). To gain a deeper understanding of the data structure, the K-means algorithm is employed for clustering and analysis. K-means clustering is a popular exploratory data analysis technique that aims to group samples into clusters of equal variances by minimizing inertia or the sum of squares within each cluster. The combination of these techniques with sensitivity analysis provides a novel analytical approach for policy formulation and decision-making. The findings have implications for practitioners and domain experts, offering valuable insights into enhancing economic resilience in the manufacturing sector.

Spatiotemporal Economic Analysis of Corn and Wheat Production in the Texas High Plains

The aim of this study is to visualize the historical changes in wheat and corn cropping patterns in the Texas High Plains from the perspective of geographical concentration and spatial autocorrelation. Historical county-level agricultural census data were collected from the United States Department of Agriculture and the National Agricultural Statistics Service from 1978 to 2017. Exploratory data analysis techniques were employed to examine the geographical concentration and spatial dependence of crop production among nearby locations. The results of temporal changes indicate that the harvested acres of corn and wheat tended to decrease throughout the study period. Total and irrigated harvested corn and wheat acreages were concentrated in a smaller number of counties over time while wheat production was mainly concentrated in the northern part of the region. The Moran’s I test statistic for total and irrigated areas of cropland suggest that there was spatial dependence among the neighboring counties in crop production in this region. In summary, there was a spatiotemporal change in cropping patterns in the Texas High Plains over the study period. Based on the results of the spatiotemporal changes in cropping patterns in the Texas High Plains, policy makers should promote and support non-irrigated varieties of crops in order to decrease the dependence on irrigation water from the Ogallala Aquifer.

Diabetes health care specific services readiness and availability in Kenya: Implications for Universal Health Coverage.

Diabetes is a major cause of morbidity and mortality worldwide yet preventable. Complications of undetected and untreated diabetes result in serious human suffering and disability. It negatively impacts on individual's social economic status threatening economic prosperity. There is a scarcity of data on health system diabetes service readiness and availability in Kenya which necessitated an investigation into the specific availability and readiness of diabetes services. A cross sectional descriptive study was carried out using the Kenya service availability and readiness mapping tool in 598 randomly selected public health facilities in 12 purposively selected counties. Ethical standards outlined in the 1964 Declaration of Helsinki and its later amendments were upheld throughout the study. Health facilities were classified into primary and secondary level facilities prior to statistical analysis using IBM SPSS version 25. Exploratory data analysis techniques were employed to uncover the distribution structure of continuous study variables. For categorical variables, descriptive statistics in terms of proportions, frequency distributions and percentages were used. Of the 598 facilities visited, 83.3% were classified as primary while 16.6% as secondary. A variation in specific diabetes service availability and readiness was depicted in the 12 counties and between primary and secondary level facilities. Human resource for health reported a low mean availability (46%; 95% CI 44%-48%) with any NCDs specialist and nutritionist the least carder available. Basic equipment and diagnostic capacity reported a fairly high mean readiness (73%; 95% CI 71%-75%) and (64%; 95%CI 60%-68%) respectively. Generally, primary health facilities had low diabetic specific service availability and readiness compared to secondary facilities: capacity to cope with diabetes increased as the level of care ascended to higher levels. Significant gaps were identified in overall availability and readiness in both primary and secondary levels facilities particularly in terms of human resource for health specifically nutrition and laboratory profession.