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A total of 13,170 participants were recruited (n=5780 people infected to SARS-COV-2 (case) and n=7390 individuals without SARS-COV-2 (control)). Based on Table 1, participants with SARS-COV-2 were significantly older than the control group (59.298.54 versus 56.979.03 years, respectively). In addition, BMI, diastolic blood pressure (DBP), systolic blood pressure (SBP), blood urea nitrogen (BUN), sex, smoking status, serum zinc, copper, creatinine (Cr), cholesterol, triglyceride, high sensitivity C-Reactive Protein (hs-CRP), fasting blood glucose (FBG), serum phosphorus, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), serum gamma glutamyl transferase (Gamma-GT), creatine phosphokinase (CPK), serum calcium, serum total bilirubin, serum direct bilirubin, aspartate aminotransferase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), serum uric acid and magnesium showed significant differences between groups. Several hematological factors, white blood cells (WBC), red blood cells (RBC), hemoglobin, hematocrit, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), red cell distribution width (RDW), platelet distribution width (PDW), and mean platelet volume (MPV) were higher compared to the control group (P-value<0.05).

We have attempted to use the LR, DT, and BF models to diagnostic COVID-19 tested participants and their biochemical and hematologic features. In this regard, the data were divided into two parts as training and test data (80%-20%), randomly. The models are validated using test data (20%) and built on the training dataset. Results of the LR algorithm illustrated that biochemical factors (Model I), such as age, smoking status, sex, DBP, SBP, BUN, BMI, hs-CRP, FBG, HDL-C, AST, ALT, CPK, total bilirubin, iron, magnesium, and Gamma-GT were correlated with COVID-19 status (P-value<0.05). In Model I, the BMI, BUN, age variables have been defined as the most crucial variable with high OR by the LR algorithm. With a unit increase in BMI, the chance of being Cov+was 1.092 times. With a year increase in age, the chance of being Cov+was 1.048 times, and with a unit increase in BUN, the chance of being Cov+was 1.041 (see Table 2). In Model II, BMI, age, hemoglobin, hematocrit, sex, MPV, smoking status, and MCHC were significant (P-value<0.05). The hemoglobin had an OR equal to 4.292, so, the chance of being Cov+was 4.292 times. The MPV had an OR equal to 1.550, so, the chance of being Cov+was 1.550 times. Table 3 showed the other variables and values of effect. In Model III, CPK, BMI, MPV, FBG, sex, BUN, Cr, iron, magnesium, total bilirubin, hemoglobin, hematocrit, MCHC, smoking status, age, WBC, HDL-C, and ALT were correlated with COVID-19 status (P-value<0.05). The total bilirubin and MPV had an OR 1.647 and 1.447, so, the chance of being Cov+was 1.647 and 1.447 times, respectively (see Table 4). Based on Table 5, for LR algorithm the accuracy of three models (Model I, II, and III) were 75.13%, 68.28%, and 69.63%, respectively. The other performance indices were given in Table 5 (a), (d), and (g).

In the training phase of DT, the important variables were selected and the final tree is given after pruning. Models I, II, and III runs with 17, 8, and 18 variables as input, respectively. In Model I, CPK, age, BUN, BMI, ALP, sex, total bilirubin, hs-CRP, FBG, and Gamma-GT, in Model II, age, MPV, sex, BMI, hemoglobin, and MCHC, and in Model III, CPK, Cr, BUN, BMI, FBG, age, MPV, MCHC, sex, and total bilirubin variables remained in models. Based on Table 5, the tree is made based on biochemical, hematologic, and both of the variables (Model I, Model II, and Model III, respectively) that had 73.24%, 70.53%, and 68.80% accuracy on the training data, respectively. The other performance indices were given in Table 5 (b), (e), and (h).

The rules from DTs for Model I, II, and III is shown in Table 6. Rule 1 in Model I was illustrated that in a subgroup with CPK>=114.09 & BUN>=30.00 & BMI>=26.77 & Age>=54.00 & Gamma-GT>=16.91, the chance or probability of having Cov+was 84.69%. In another subgroup, CPK<114.09 & CPK<88.06 & Sex(female) & ALT<9.00 led to a 6.57% chance of having Cov+. The rules from Model II, were illustrated that there was an 86.46% chance that participants with features such as Age>=54.00 & BMI>=26.77 & MPV>=9.60 & Sex(male) & Hemoglobin<15.8 be infected with COVID-19. Another rule was suggested that the probability of Cov+in individuals with Age<54.00 & MPV<9.10 was 12.26%. The rules from Model III, were illustrated that there was an 88.15% chance that participants with features such as CPK>=114.09 & BUN>=30.00 & BMI>=26.77 & Age>=54.00 & MPV>=9.60 & MCHC<35.6 be infected with COVID-19. Another rule was suggested that the probability of Cov+in individuals with CPK<114.09 & Cr<1.40 & Cr<1.00 & FBG<118.34 & Sex(female) was 9.90%. Other rules were stated in Table 6.

Hence, the CPK and BUN for Model I, age, BMI, and MPV for Model II, and CPK and BUN for Model III were defined as most crucial variables. The final DT is shown in Figs.2, 3, and 4.

Graphical representation of the classification tree introduced for SARS-COV-2 diagnosis for Model I

Graphical representation of the classification tree introduced for SARS-COV-2 diagnosis for Model II

Graphical representation of the classification tree introduced for SARS-COV-2 diagnosis for Model III

In the final step, for another analysis we applied BF for analyzing the data based on COVID-19. The factors included in the BF algorithm were 17, 8, and 18 variables for Model I, II, and III, respectively. Moreover, we set the following specifications for Model I: Number of Trees in the Forest: 29 for Model I, 13 for Model II, and 53 for Model III, Number of Terms Sampled per Split: 4 for Model I, 2 for Model II, and 4 for Model III, Training Rows: 10,536, Test Rows: 2634, Minimum Splits per Tree: 10, Minimum Size Split: 13 for all three models. Confusion matrix and evaluation indices for comparison of the models I, II, III were stated in Table 5 (c), (f), and (i). Additionally, the crucial variables related to COVID-19 based on BF algorithm were: CPK, BUN, FBG, BMI, total bilirubin, and age in Model I, BMI, sex, MPV, and age in Model II, and CPK, Cr, FBG, BMI, BUN, total bilirubin, sex, MPV, and age for Model III. As one can check the obtained features from BF algorithm were equal to the obtained factors from LR and DT algorithms.

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Association between biochemical and hematologic factors with COVID-19 using data mining methods - BMC Infectious ... - BMC Infectious Diseases

In the dynamic landscape of data mining, staying ahead of the curve is crucial for extracting meaningful insights efficiently. While WEKA has been a stalwart in the field, 2024 heralds the arrival of more advanced and versatile data mining tools. This article explores the evolving data mining terrain and presents a curated list of tools that outshine WEKA in the current technological landscape.

RapidMiner stands tall among data scientists for its user-friendly interface and powerful capabilities. With an extensive library of pre-built templates, it simplifies complex data mining tasks, making it a go-to choice for both beginners and experts.

KNIME, an open-source platform, has gained popularity for its flexibility and adaptability. With a modular workflow design, it enables seamless integration of various data mining components, offering a collaborative environment for data scientists and analysts.

Orange is celebrated for its visual programming interface, making it an ideal choice for those who prefer a graphical approach to data mining. With an array of visualizations, it allows users to comprehend complex patterns and relationships effortlessly.

SAS Enterprise Miner empowers organizations with robust data mining capabilities. Known for its advanced analytics and machine learning algorithms, it is a comprehensive tool for businesses seeking in-depth insights from their data.

While TensorFlow is renowned for its prowess in machine learning, its data mining capabilities have made significant strides. Widely adopted by developers and data scientists, TensorFlow offers a scalable and efficient platform for mining valuable patterns from vast datasets.

In the era of big data, Apache Spark MLlib stands out as a data mining tool capable of handling massive datasets with ease. Leveraging the power of Spark, it enables distributed data mining, making it a robust choice for organizations dealing with large-scale data.

Bringing data mining to the cloud, Microsoft Azure Machine Learning provides a scalable and efficient platform for extracting insights. With seamless integration with other Azure services, it simplifies the end-to-end data mining process.

For Python enthusiasts, Scikit-Learn remains a top choice. Its simplicity and integration with popular Python libraries make it an accessible yet powerful tool for data mining tasks.

As we bid farewell to the era where WEKA reigned supreme, the data mining landscape in 2024 is brimming with innovative and powerful tools. Whether you prioritize user-friendliness, open-source flexibility, or cloud-powered scalability, the tools mentioned above offer a diverse range of options to cater to your data mining needs. Embrace the future of data mining with these cutting-edge tools and unlock the full potential of your datasets.

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WEKA is Outdated: Here are the Best Data Mining Tools for 2024 - Analytics Insight

An assessment of trends in the mining sector during 2023 reveals an industry grappling with rising supply costs and changing demand as the world shifts away from coal and towards renewable energies.

Inflation is driving up the prices of core products and services, in particular fuel and power, as well as maintenance and explosives. Meanwhile, metals used in batteries are a growing market and the net-zero energy transition is bringing with it new technologies and sustainability expectations.

According to GlobalData analytics, there were a total of 511 asset transactions in the mining sector between 1 January 2023 and 15 December 2023, with a value totaling $29,393m. Of these, 305 were acquisitions, worth $70,931m in total, and 12 were mergers, worth $565m.

On the trends visible within the 2023 deals, David Kurtz, Director of Mining and Construction at GlobalData, says: On mining M&A were seeing an increasing shift towards transition metals and battery commodities. Looking at announced M&A, we can see a roughly 40% increase in deals across lithium, cobalt and nickel this year compared to 2022, and its about four times higher compared to 2019, in both value and volume terms, as companies focus their investments on these battery metals as well as other future-facing commodities.

Indicative of these changing priorities in the industry, the highest value deal in the sector in 2023 was Coolabah Metals acquisition of full ownership of the Cannington Project in Australia. Coolabah Metal is an Australia-based minerals exploration company which identifies, acquires and develops copper, gold and base metal assets; it acquired the project from Thomson Resources on 15 March for $19,994.7m.

GlobalData analysis also indicates that gold saw the most deals by sector in 2023, with 235 deals. It was followed by copper, which saw 163, silver with 67, nickel with 53, and coal with 50.

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2023 saw a general decline in stock prices, as the prices of base metals followed a mostly downward trajectory over the year. This is a result of lower demand from Chinas heavy industry and real estate sectors, which have suffered the impacts of an economy hit by youth unemployment, and slow growth post-Covid.

In its recent report, the World Bank noted that metal prices fell 2% in Q3 2023, compared to Q2, following an overall downward trend. It expects an overall decline of 12% over the course of the year, and anticipates that this decline will continue into 2024, due to continued slowing demand from Chinese markets.

However, demand for minerals needed for battery production such as lithium, copper and nickel will continue to increase. The World Bank says: In the short-term, they have followed the downward trend in base metals prices [However,] firming global growth, along with policies to expand renewable energy infrastructure, are expected to underpin a rebound in metal and mineral prices in 2025. Global investment in clean energy infrastructure has grown by almost 28%between 2021 and 2023 and continues to rise rapidly, propelling a demand surge for copper, lithium, and nickel.

The launch of ChatGPT in November 2022 thrust Artificial Intelligence (AI) into the limelight for almost all sectors this year. In mining, AI received a consistently high number of mentions across company filings throughout the year.

Early examples of AI are already disrupting the mining industry: a GlobalData report noted the use of AI in resource expansion by SensOre, an Australian mineral resource company. The company has used AI and machine learning technologies on its giant Western Australia multidimensional data cube to identify lithium-rich pegmatite signatures during early field reconnaissance.

GlobalData also noted AI use by Benchmark Metals Inc, a Canadian provider of mineral resource exploration. The company has initiated an AI-guided, 20,000-metr drilling program to define high-grade gold and silver zones at Cliff Creek and Dukes Ridge in British Columbia, Canada.

AI is still in its infancy in mining, but the industry has high expectations. A recent GlobalData poll found that that 53% of respondents expect AI to live up to its promises, while only 6% considered it all hype and no substance.

The mining sector has seen a decline in active roles across 2023, in a downward trend driven by the steady phasing out of coal.

In particular, there was drop in active roles across Australias mining sector. The sector supported 4,007 jobs in June 2023, but had seen a 38.8% decrease in positions by November, when only 2,452 roles were active. This saw Germany overtake Australia in September, with 2,852 roles; Canada overtook it with 2,671 in September.

However, some parts of the industry saw a growth in jobs. The global mining supply chain grew from 6,085 jobs in January 2023 to 7,121 by June 2023. Environment-related jobs also grew, from 7,610 in January to 9,606 in June.

Within the supply chain, the highest number of active roles were for logisticians and project management specialists, followed by buyers and purchasing agents. Trends within the supply chain were likely driven by difficulties sourcing products and services; a recent survey found that 41% of respondents agreed that difficulties had motivated them to seek out more new suppliers in 2023 than in the past, and 27% strongly agreed.

Meanwhile, in the environment department, maintenance and repair workers had the most active roles, followed by electrical and electronics engineers. The aforementioned survey confirmed this trend in the data, finding that 83% of respondents had looked for (an) alternative supplier(s) in maintenance and repair in 2023, compared to 70% in 2022.

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2023 in data: the trends that shaped the mining sector - Mining Technology