F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.15620.1 Research Article Articles Exploring machine learning: A bibliometric general approach using SciMAT

[version 1; peer review: 2 approved with reservations]

 Rincon-Patino Juan Conceptualization Formal Analysis Methodology Software Writing – Original Draft Preparation https://orcid.org/0000-0002-3709-4878 1 Ramirez-Gonzalez Gustavo Conceptualization Methodology Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-1338-8820 a 1 Corrales Juan Carlos Conceptualization Writing – Original Draft Preparation Writing – Review & Editing 1 Telematic Engineering Department, University of Cauca, Popayán, Cauca, 190001, Colombia gramirez@unicauca.edu.co

No competing interests were disclosed.

 7 8 2018 2018 7 1210 26 7 2018 Copyright: © 2018 Rincon-Patino J et al. 2018 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: Machine learning is becoming increasingly important for companies and the scientific community. In this study, we perform a bibliometric analysis on machine learning research, in order to provide an overview of the scientific work during the period 2007–2017 in this area and to show trends that could be the basis for future developments in the field.

Methods: This study is carried out using the SciMAT tool based on results extracted from Scopus. This analysis shows the strategic diagrams of evolution and a set of thematic networks. The results provide information on broad tendencies of machine learning.

Results: The results show that SciMAT is a useful tool to carry out a science mapping analysis, and emphasizes the premise that machine learning has boundless applications and will continue to be an interesting research field in the future.

Conclusions: Some of the conclusions exposed in this study show that classification algorithms have been widely studied and represent a relevant tool for generating different machine learning applications. Nonetheless, regression algorithms are becoming increasingly important in the scientific community, allowing the generation of solutions to predict diseases, sales, and yields, for example.

 machine learning science mapping bibliometrics topic analysis SciMAT Universidad del Cauca The authors are grateful to the Telematics Engineering Group (GIT) of the University of Cauca for scientific support and Innovacción Cauca project for master's scholarship granted to J. Rincon-Patino. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Introduction

The machine learning field researches different human learning processes, the theoretical analysis of possible learning algorithms and methods for several application domains 1 . Studies based on machine learning have allowed scientists and companies to predict mass mortality events 2 , the quality of water 3 , segment clients in private banking 4 , automatically classify text 5 or for the production of crops, such as cocoa 6 . Considering the growing interest of the scientific community in machine learning research and its challenges, it is interesting and necessary to analyze the field. A good approach for that purpose is science mapping analysis because it is a different way of visualizing information that allows a new researcher to become familiar with a field. An example of science mapping analysis providing an overview of the conceptual evolution of a field in medicine is proposed in 7. In this study, we perform a science mapping analysis to explore machine learning research. The objective of the study is to allow new data analysts to know the current knowledge base about machine learning and to have an initial point to explore applications in this field.

This article has the following structure: In the Methods section, we describe the research methodology, the dataset used, the tool configuration, and how the analysis was performed. The Results section presents the results of the science mapping analysis. The conclusions are at the end of the article.

 Methods Dataset for visualization analysis

We used Scopus as the bibliographic source. We looked, in the third quarter of 2017, for references of articles and conferences about machine learning, using this concept as the search keyword (‘machine AND learning’), with results ranging from 2007 to 2017(Q2). The concept was searched for in the article title, abstract and keywords of articles found. These articles were sorted by date (newest first). All the articles, between 2007 and 2017, were taken in to account for performing the analysis with the aim of obtaining a general vision of the field.

We obtained 67,475 records that were saved using RIS format in different files sorted by year. Figure 1 shows a summary of the records, and shows that research in the field of machine learning has been growing steadily. It is important to observe that the results for 2017 are not in the figure since the records are only to the second half Q2 of year and database is permanently updated. However, these results were used for the analysis because they show trends, which is the primary objective of the present study.

Number of documents for Scopus per year for machine learning 2007–2017(Q2). Parameter design

We used the records from Scopus for executing the analysis with SciMAT version 1.1.04. In this tool, the unit of analysis was Words. Primarily, we did a deduplication process, grouping similar words (by plurals) and looking for synonyms or duplicates in words with the highest number of documents and repetitions, always trying to avoid bias to include the largest number of terms. After that, we divided the time interval (2007 – 2017) into six smaller periods: 2007–2009, 2010–2012, 2013–2014, 2015, 2016, 2017(Q2 – published papers up the second quarter of the year). We distributed the gaps this way in order to have a comparable number of articles in each one of them. Finally, we carried out the analysis with the following configuration: all the periods, author’s words as the unit of analysis, a minimum frequency for data reduction of 100 (excepting 2017 (Q2) with 50) and co-occurrence as a kind of network. Other configurations were: network reduction equal to one, association strength as a normalization measure, Simple centers algorithm with a maximum net size of seven and a minimum of five, core mapper for the document mapper, h-index and Sum citations as the quality measures and, lastly, association strength for the evolution and overlapping map.

 Analysis design

SciMAT shows a spatial representation of the way disciplines, fields, specialties and documents or authors related to one another 8– 10 . For this purpose, the tool implements a longitudinal framework, which takes as its base a co-word analysis and the h-index. Co-word analysis firstly provides information on the themes of a research field and, secondly, enables to analyze and to track the evolution of a study field throughout consecutive periods of time 11 . The h-index is used to measure the impact of the various identified thematic areas 8 .

In this tool, we follow four steps as mentioned by 10: researching cluster detection, drawing strategic diagrams, plotting of thematic areas and carrying out a performance analysis. For the first one, the tool creates a network of keywords co-occurrence based on 12 and 13 and makes a clustering of keywords to topics, using the Simple center's algorithm. For the second step, according to 13, the cluster centrality and density rank values are relevant. The centrality measures the intensity of the interaction of a group with the others; if the cluster is boldly related to the field of research, then the link will be stronger. The density measures the intensity of internal links inside the group. According to 8 and 10, the themes of the clusters can be classified into four groups using these two measures: (1) Motor themes, which are both well developed and central to the research field; (2) basic and transversal themes, which are not sufficiently developed topics but significant for the area of investigation; (3) emerging or declining themes, which are weakly studied and marginal; (4) highly developed and isolated themes, which have well-developed internal links, but reduced external links and have only minimal importance to the field. The third step is the plotting of thematic areas, and the last one is to conduct the performance analysis. In this study, we analyzed quantitative measures, such as the number of documents and authors.

 Results

To analyze the most relevant topics of investigation in different years, SciMAT uses strategic diagrams. We generated a diagram for each period of the study. The charts are divided into four quadrants: the upper-right quadrant alludes to the motor themes, the upper-left quadrant to the highly developed and isolated themes, the lower-right one to the basic and transversal themes, and the lower-left one to the emerging or declining topics 9 .

Figure 2 shows the strategic diagram of the period 2007–2009, which has 27 themes. During this time span, OPTIMIZATION, CONTROL-THEORY, and IMAGE-CLASSIFICATION are some of the emerging topics. SEQUENCE-ANALYSIS-PROTEIN appears as the motor theme with the highest density (0.83) and centrality (3.21) values, followed by DATABASES-PROTEIN, with centrality equal to 2.97 and density equal to 0.43. This suggests that machine learning was widely used to study the protein molecule in that period, covering, for example, studies to predict the structure or function of that molecule.

Strategic diagram of the period 2007–2009 for machine learning using data from Scopus.

Due to the great number of themes that appear in the strategic diagram, we decided to choose three themes, giving priority to those that may have a high impact application. Figure 3A shows the net for the theme DATABASES-PROTEIN, which has a density of 0.43, a centrality of 2.97 and a document count of 322. In this net, we can observe that an important topic is PROTEIN-STRUCTURE. Figure 3B presents the network for the theme DATASETS, which has a density of 0.06, a centrality of 1.37, a document count of 273 and strongly related topics such as CLASSIFIERS, SEMI-SUPERVISED-LEARNING and RANDOM-FOREST. Figure 3C shows the network for the theme IMAGE-CLASSIFICATION, which has a density of 0.11, a centrality of 1.3, a document count of 273 and relevant concepts, such as NEURAL-NETWORKS, SUPPORT-VECTOR-MACHINE (SVM) and IMAGE-PROCESSING.

Selected thematic networks for period 2007–2009 for machine learning using data from Scopus.

( A) DATABASES-PROTEIN; ( B) DATASETS; ( C) IMAGE-CLASSIFICATION.

Figure 4 shows the strategic diagram of the period 2010–2012. The diagram has 35 themes. During this time span, VIRTUAL-REALITY, ROBOTS, and METADATA are some of the emerging themes, while CLASSIFICATION-ALGORITHM is one of the basic and transversal topics. AGED and PROTEIN-ANALYSIS appear as the motor subjects with the highest density (0.72 and 0.58, respectively) and centrality (1.91 and 2.18, respectively) values. Other important themes are CHEMISTRY AND GENETICS. This is a sign that during this period, topics on biology and health began to become relevant in applied machine learning research.

Strategic diagram of the period 2010–2012 for machine learning using data from Scopus.

From the second period, we selected three thematic networks. Figure 5A shows the net for the theme PROTEIN-ANALYSIS, which has a density of 0.58, a centrality of 2.18 and a document count of 229. This shows us that topics about proteins continue to be important in this period. Figure 5B presents the network for the theme CHEMISTRY, which is an emergent theme and has a density of 0.32, a centrality of 2.24 and a document count of 346. Figure 5C shows the network for the subject VIRTUAL-REALITY, which has a density of 0.07, a centrality of 0.98, a document count of 65 and is another emerging theme for the period 2010–2012.

Selected thematic networks for period 2010–2012 for machine learning using data from Scopus.

( A) PROTEIN-ANALYSIS; ( B) CHEMISTRY; ( C) VIRTUAL-REALITY.

The strategic diagram of the period 2013–2014 is shown on Figure 6, which has 32 themes. During this time frame, SENSORS, FACE-RECOGNITION and COMMERCE are some of the emerging topics. The IMAGE-INTERPRETATION-COMPUTER-ASSISTED topic appears as the motor theme, with the highest density (0.58) and centrality (2.61) values. This suggests that in this period there were studies on machine learning applied to different topics, such as sensor data, costs, and gesture recognition.

Strategic diagram of the period 2013–2014 for machine learning using data from Scopus.

We selected three thematic networks from the third period (2013–2014). Figure 7A shows the network for the theme AMINO-ACID-SEQUENCE, which has a density of 0.42, a centrality of 1.91 and a document count of 276. Figure 7B presents the network for the theme MOBILE-DEVICES, which has a density of 0.21, a centrality of 0.76, a document count of 159 and strongly related topics such as HUMAN-COMPUTER-INTERACTION, E-LEARNING, and UBIQUITOUS-COMPUTING. Figure 7C shows the network for the theme COMMERCE, which has a density of 0.07, a centrality of 0.88, a document count of 205 and relevant concepts, such as SOCIAL-NETWORKING and COSTS.

Selected thematic networks for period 2013–2014 for machine learning using data from Scopus.

( A) AMINO-ACID-SEQUENCE; ( B) MOBILE-DEVICES; ( C) COMMERCE.

Figure 8 shows the strategic diagram of the period 2015. The diagram has 25 themes. During this time span, SMARTPHONES, FACE-RECOGNITION and FORESTRY (label generated for algorithms such as Random-Forest or Decision-Trees) are some of the emerging themes. NUCLEAR-MAGNETIC-RESONANCE-IMAGING appears as the motor subject with the highest density (0.57) and centrality (2.81) values. Other important themes are COMPUTATIONAL-BIOLOGY and MEDICAL-IMAGING. We found that during this period biology and health were once again relevant topics in machine learning research.

Strategic diagram of the period 2015 for machine learning using data from Scopus.

From the fourth time frame, we selected three thematic networks. Figure 9A shows the network for the theme NUCLEAR-MAGNETIC-RESONANCE-IMAGING, which has a density of 0.57, a centrality of 2.81 and a document count of 251. Figure 9B presents the network for the theme COMPUTATIONAL-BIOLOGY, which is an emergent theme and has a density of 0.44, a centrality of 1.8 and a document count of 320. Figure 9C shows the network for the topic SMARTPHONES, which has a density of 0.07, a centrality of 0.13, a document count of 144 and is another emerging theme for the 2015 period.

Selected thematic networks for period 2015 for machine learning using data from Scopus.

( A) NUCLEAR-MAGNETIC-RESONANCE-IMAGING; ( B) COMPUTATIONAL-BIOLOGY; ( C) SMARTPHONES.

The strategic diagram of the period 2016 is shown in Figure 10, which has 24 themes. During this time span, UBIQUITOUS-COMPUTING and COMMERCE are some of the emerging themes. The MIDDLE-AGED theme –which refers to applications developed for middle-aged people– appears as the motor topic with the highest density (0.76) and centrality (2.04) values.

Strategic diagram of the period 2016 for machine learning using data from Scopus.

We selected three thematic networks from the fifth period (2016). Figure 11A shows the network for the theme MEDICAL-IMAGING, which has a density of 0.26, a centrality of 1.45 and a document count of 312. Figure 11B presents the network for the theme INTRUSION-DETECTION, which has a density of 0.3, a centrality of 0.89 and a document count of 289. Figure 11C shows the network for the theme UBIQUITOUS-COMPUTING, which has a density of 0.07, a centrality of 1.07, a document count of 132 and relevant concepts, such as AUTOMATION, SMARTPHONES and the INTERNET.

Selected thematic networks for period 2016 for machine learning using data from Scopus.

( A) MEDICAL-IMAGING; ( B) INTRUSION-DETECTION; ( C) UBIQUITOUS-COMPUTING.

Figure 12 shows the strategic diagram of the period 2017(Q2). The diagram has 16 themes. During this time span, HUMAN is the only emerging theme, while FORESTRY is one of the basic and transversal topics. This shows us the importance of algorithms such as Random-Forest or Decision-Trees during the last decade in the research on machine learning. MEDICAL-IMAGING appears as the motor theme with the highest density (0.51) and centrality (2.05) values. Once again, topics on health are relevant in machine learning research.

Strategic diagram of the period 2017(Q2) for machine learning using data from Scopus.

From 2017(Q2), we selected three thematic networks. Figure 13A shows the net for the theme MEDICAL-IMAGING, which has a document count of 137. Figure 13B presents the network for the topic FORESTRY (label generated for algorithms such as Random-Forest or Decision-Trees), which has a density of 0.17, a centrality of 1.84 and a document count of 220.

Selected thematic networks for period 2017(Q2) for machine learning using data from Scopus.

( A) MEDICAL-IMAGING; ( B) FORESTRY.

 Conclusions

Exposing emerging trends in the field of machine learning allows researchers to increase their understanding of the changes and the evolution over time of this research field. One of the primary objectives of a science mapping analysis is to highlight trends and possible relationships between the relevant topics of a research field. SciMAT is a useful tool to carry out a study based on this approach, which offers fundamental themes, based on a cluster generation. The results of the present study show that machine learning is an important and widely studied scientific area. The tendencies indicate that machine learning applications will still be of interest to the scientific community. The use of machine learning to predict diseases such as cancer 14 or Alzheimer’s disease 15 , and in fields such as biology 16 , rehabilitation system 17 , commerce 18 , smartphones 19 and ubiquitous computing 20 , will be a trend in the near future.

 Data availability

The data referenced by this article are under copyright with the following copyright statement: Copyright: � 2018 Rincon-Patino J et al.

Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

Dataset 1: Data obtained from Scopus and SciMat project file, to be opened in SciMat. DOI, 10.5256/f1000research.15620.d212425 21

Michalski RS Carbonell JG Mitchell TM : Machine Learning: An Artificial Intelligence Approach.Springer Berlin Heidelberg,2013. 10.1007/978-3-662-12405-5 Crisci C Ghattas B Perera G : A review of supervised machine learning algorithms and their applications to ecological data. Ecol Modell. 2012;240:113–122. 10.1016/j.ecolmodel.2012.03.001 López ID Figueroa A Corrales JC : Adaptive Prediction of Water Quality Using Computational Intelligence Techniques. In Computational Science and Its Applications -- ICCSA 2017: 17th International Conference, Trieste, Italy, July 3-6, 2017, Proceedings, Part II,Cham: Springer International Publishing,2017;45–59. 10.1007/978-3-319-62395-5_4 Smeureanu I Ruxanda G Badea LM : Customer segmentation in private banking sector using machine learning techniques. J Bus Econ Manag. 2013;14(5):923–939. 10.3846/16111699.2012.749807 Sebastiani F : Machine Learning in Automated Text Categorization. ACM Comput Surv. 2002;34(1):1–47. 10.1145/505282.505283 Plazas JE López ID Corrales JC : A Tool for Classification of Cacao Production in Colombia Based on Multiple Classifier Systems.In Computational Science and Its Applications -- ICCSA 2017: 17th International Conference, Trieste, Italy, July 3-6, 2017, Proceedings, Part II,Cham: Springer International Publishing,2017;60–69. 10.1007/978-3-319-62395-5_5 Moral-Muñoz JA Cobo MJ Peis E : Analyzing the research in Integrative & Complementary Medicine by means of science mapping. Complement Ther Med. 2014;22(2):409–418. 24731913 10.1016/j.ctim.2014.02.003 Martínez MA Cobo MJ Herrera M : Analyzing the Scientific Evolution of Social Work Using Science Mapping. Res Soc Work Pract. 2015;25(2):257–277. 10.1177/1049731514522101 Cobo MJ López-Herrera AG Herrera-Viedma E : SciMAT: A new science mapping analysis software tool. J Am Soc Inf Sci Technol. 2012;63(8):1609–1630. 10.1002/asi.22688 Cobo MJ López-Herrera AG Herrera-Viedma E : An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. J Informetr. 2011;5(1):146–166. 10.1016/j.joi.2010.10.002 Garfield E : Scientography: Mapping the tracks of science. Curr Contents Soc Behav Sci. 1994;7(45):5–10. Reference Source Callon M Courtial JP Turner WA : From translations to problematic networks: An introduction to co-word analysis. Soc Sc Inform. 1983;22(2):191–235. 10.1177/053901883022002003 Callon M Courtial J Laville F : Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry. Scientometrics. 1991;22(1):155–205. 10.1007/BF02019280 Wang Y Tetko IV Hall MA : Gene selection from microarray data for cancer classification—a machine learning approach. Comput Biol Chem. 2005;29(1):37–46. 15680584 10.1016/j.compbiolchem.2004.11.001 Moradi E Pepe A Gaser C : Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. Neuroimage. 2015;104:398–412. 25312773 10.1016/j.neuroimage.2014.10.002 5957071 Swan AL Mobasheri A Allaway D : Application of Machine Learning to Proteomics Data: Classification and Biomarker Identification in Postgenomics Biology. OMICS. 2013;17(12):595–610. 24116388 10.1089/omi.2013.0017 3837439 Yeh SC Huang MC Wang PC : Machine learning-based assessment tool for imbalance and vestibular dysfunction with virtual reality rehabilitation system. Comput Methods Programs Biomed. 2014;116(3):311–318. 24894180 10.1016/j.cmpb.2014.04.014 Yan J Zhang C Zha H : On Machine Learning Towards Predictive Sales Pipeline Analytics. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence.2015;1945–1951. Reference Source Faragher RM Harle RK : SmartSLAM - An Efficient Smartphone Indoor Positioning System Exploiting Machine Learning and Opportunistic Sensing. In Proceedings of the 26th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2013).2013;1006–1019. Reference Source Ventura D Casado-Mansilla D López-de-Armentia J : ARIIMA: A Real IoT Implementation of a Machine-Learning Architecture for Reducing Energy Consumption. In Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services: 8th International Conference, UCAmI 2014, Belfast, UK, Cham: Springer International Publishing,2014;444–451. 10.1007/978-3-319-13102-3_72 Rincon-Patino J Ramirez-Gonzalez G Corrales JC : Dataset 1 in: Exploring machine learning: A bibliometric general approach using SciMAT. F1000Research. 2018. http://www.doi.org/10.5256/f1000research.15620.d212425 10.5256/f1000research.17040.r37068 Reviewer response for version 1 Dozmorov Mikhail G. 1 Referee https://orcid.org/0000-0002-0086-8358 Department of Biostatistics , Virginia Commonwealth University, Richmond, VA, USA

Competing interests: No competing interests were disclosed.

 15 8 2018 Copyright: © 2018 Dozmorov MG 2018 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

The paper “Exploring machine learning: A bibliometric general approach using SciMAT” by Rincon-Patino et al. presents an overview of areas of machine learning applications. The study is conducted using science mapping analysis with the SciMAT tool. A bibliography containing “machine” and “learning” keywords over the 10-year period (2007-2017) was extracted from Scopus. This period was split into six smaller periods, and the state of machine learning in each period was illustrated by two figures, strategic diagram, and selected thematic network. The paper is well written. The following recommendations are intended to improve the readability and message of the paper.

Strategic diagrams may not be familiar for users who aren’t familiar with the SciMAT tool. Thus, the description of strategic diagrams given at the beginning of the Results section is better to be placed to the legend of the first figure. Also, clarify the meaning of “the basic and transversal themes.” Furthermore, the description of the lower-left quadrant, “the emerging or declining topics,” is confusing, it should be one or the other.

Although the study claims to present a longitudinal analysis of the development of machine learning areas, it is the analysis and the description of individual time periods, presented on individual figures. I am missing the compare-and-contrast view of the analysis, which would have been possible if the figures would be presented side-by-side. Given the good readability of the strategic diagrams, it is perfectly possible to combine them into one figure. The same can be done for the selected thematic networks, by making edges shorter, thicker, and fonts - larger.

It is unclear whether the manuscript is about the overview of machine learning fields, or the tool SciMAT and the science mapping analysis. Besides simply describing what the results are at each time period, it would be good to have some conclusions about the longitudinal trends of the field.

Although the rationale for choosing the non-equal smaller periods in understandable, the periods themselves should be better defined. At the very least, state that the presented periods are inclusive, that is, 2007-2009 spans the 3-year period. Better, adopt (MM/DD/YYYY-MM/DD/YYYY] notation.

Figure 1 shows the growing number of documents containing “machine” and “learning” keywords. However, it is known that the number of publications per year grows by itself. It would be more informative to show the proportion of published documents that contain machine" and “learning” keywords. The usefulness of representing the proportion of publications can be illustrated with the following R code:

 library(MDmisc) # devtools::install_github('mdozmorov/MDmisc')

library(ggplot2)

p <- get_pubmed_graph("machine learning", yearstart = 2007, yearend = 2017, normalize = TRUE, xlab = "Year", ylab = "Proportion of all publications")

p

ggsave("pubmed_machine_learning.png", p, device = "png", height = 4)

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

NA

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

 10.5256/f1000research.17040.r36927 Reviewer response for version 1 Tiwari Rajesh Kumar 1 Referee Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, India

Competing interests: No competing interests were disclosed.

 9 8 2018 Copyright: © 2018 Tiwari RK 2018 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

The article highlights about the bibliometric analysis of scientific works in the area of  machine learning, published in Scopus indexed Journals during the period of 2007-2017. The manuscript is well written. However, I have following suggestions to make:

 

Correction in title of Manuscript: Exploring machine learning: A bibliometric general approach using SciMAT Tool

Definition of Machine learning should be incorporated in the introduction Part. Authors can refer and cite following articles: PMID: 23740390 1  and PMID: 28302041 2

Apart from mass  mortality  events,  the  quality  of  water,  segment  clients  in  private  banking,  automatically  classify  text, production  of  crops, the machine learning applications are also widely used in the Drug designing in pharmaceutical industries, academia and research. Authors should mention the same in the introduction part of the article and can refer and cite the following articles with PMID: 22346230 3 , PMID: 26526829 4 , PMID: 28382857 5 , PMID: 29256344 6

Methods: As the data set is collected from Scopus, the same should be cited in the manuscript.

The original source of SciMAT Tool should be cited in the manuscript.

The authors must elaborate more about the basis of division of the data set. It  is mentioned  that the  time  interval divided  into  six  smaller  periods:  2007–2009,  2010–2012,  2013–2014,  2015,  2016,  2017. As the  division is not uniform. Was the gap made such a way so as to have comparable number  of  articles  in  each  one  of  them only?

The machine learning algorithms are widely used in robotics and pharmaceutical properties prediction. They can also be included in conclusion part.

Apart from SciMAT, are some other similar tools available? Can the comparative  analysis of results using the same data set from different tools be done to know that how conclusive the reported results are? This can be included as future prospect of the current research.         

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Machine learning classification, prediction models using Artificial Intelligence, Statistical analysis, Computational Biology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

References : Can we predict blood brain barrier permeability of ligands using computational approaches?. Interdiscip Sci .2013;5(2) : 10.1007/s12539-013-0158-9 95-101 23740390 10.1007/s12539-013-0158-9 : Promises of Machine Learning Approaches in Prediction of Absorption of Compounds. Mini Rev Med Chem .2018;18(3) : 10.2174/1389557517666170315150116 196-207 28302041 10.2174/1389557517666170315150116 : A prediction model for oral bioavailability of drugs using physicochemical properties by support vector machine. J Nat Sci Biol Med .2011;2(2) : 10.4103/0976-9668.92325 168-73 22346230 10.4103/0976-9668.92325 : Prediction of Metabolism of Drugs using Artificial Intelligence: How far have we reached?. Curr Drug Metab .2016;17(2) :129-41 26526829 : Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques. Curr Drug Discov Technol .2017;14(4) : 10.2174/1570163814666170404160911 244-254 28382857 10.2174/1570163814666170404160911 : Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms. Comb Chem High Throughput Screen .2018;21(1) : 10.2174/1386207321666171218121557 57-64 29256344 10.2174/1386207321666171218121557