{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:50:37Z","timestamp":1742950237012,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031440830"},{"type":"electronic","value":"9783031440847"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-44084-7_25","type":"book-chapter","created":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T09:02:09Z","timestamp":1695459729000},"page":"261-268","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Seasonal Disease Based Demand Forecasting for Pharmaceutical Medications Using Random Forest"],"prefix":"10.1007","author":[{"given":"R.","family":"Sakthi Ganesh Dharani","sequence":"first","affiliation":[]},{"given":"S. V.","family":"Lokheshram","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3324-5317","authenticated-orcid":false,"given":"A.","family":"Malini","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,24]]},"reference":[{"key":"25_CR1","doi-asserted-by":"publisher","DOI":"10.1080\/0952813X.2022.2121426","author":"A Manocha","year":"2022","unstructured":"Manocha, A., Afaq, Y., Bhatia, M.: Intelligent analysis of irregular physical factors for panic disorder using quantum probability. J. Exp. Theor. Artif. Intell. (2022). https:\/\/doi.org\/10.1080\/0952813X.2022.2121426","journal-title":"J. Exp. Theor. Artif. Intell."},{"issue":"01","key":"25_CR2","doi-asserted-by":"publisher","first-page":"20","DOI":"10.38094\/jastt20165","volume":"2","author":"B Charbuty","year":"2021","unstructured":"Charbuty, B., Abdulazeez, A.: Classification based on decision tree algorithm for machine learning. J. Appl. Sci. Technol. Trends 2(01), 20\u201328 (2021). https:\/\/doi.org\/10.38094\/jastt20165","journal-title":"J. Appl. Sci. Technol. Trends"},{"key":"25_CR3","unstructured":"Segal, M.R.: UCSF recent work title machine learning benchmarks and random forest regression publication date machine learning benchmarks and random forest regression (2003)"},{"key":"25_CR4","unstructured":"Keny, S., Nair, S., Nandi, S., Khachane, D.: Sales prediction for a pharmaceutical distribution company. Int. J. Eng. Appl. Phys. (IJEAP) 1(2), 186\u2013191 (2021). https:\/\/ijeap.org\/"},{"key":"25_CR5","doi-asserted-by":"publisher","first-page":"10458","DOI":"10.1109\/ACCESS.2018.2808843","volume":"6","author":"B Graham","year":"2018","unstructured":"Graham, B., Bond, R., Quinn, M., Mulvenna, M.: Using data mining to predict hospital admissions from the emergency department. IEEE Access 6, 10458\u201310469 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2808843","journal-title":"IEEE Access"},{"key":"25_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijmedinf.2017.01.001","volume":"100","author":"FR Lucini","year":"2017","unstructured":"Lucini, F.R., et al.: Text mining approach to predict hospital admissions using early medical records from the emergency department. Int. J. Med. Inform. 100, 1\u20138 (2017). https:\/\/doi.org\/10.1016\/j.ijmedinf.2017.01.001","journal-title":"Int. J. Med. Inform."},{"issue":"5","key":"25_CR7","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1002\/widm.23","volume":"1","author":"R Bellazzi","year":"2011","unstructured":"Bellazzi, R., Ferrazzi, F., Sacchi, L.: Predictive data mining in clinical medicine: a focus on selected methods and applications. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 1(5), 416\u2013430 (2011). https:\/\/doi.org\/10.1002\/widm.23","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"issue":"S1","key":"25_CR8","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1080\/20479700.2018.1531608","volume":"13","author":"M Yucesan","year":"2020","unstructured":"Yucesan, M., Gul, M., Celik, E.: A multi-method patient arrival forecasting outline for hospital emergency departments. Int. J. Healthc. Manage. 13(S1), 283\u2013295 (2020). https:\/\/doi.org\/10.1080\/20479700.2018.1531608","journal-title":"Int. J. Healthc. Manage."},{"key":"25_CR9","unstructured":"Muriithi, I.A., Muchemi, L.: School of computing and informatics a data mining approach to private healthcare services demand forecast in Nairobi County (2014)"},{"key":"25_CR10","doi-asserted-by":"publisher","unstructured":"Cheng, C.Y., Chiang, K.L., Chen, M.Y.: Intermittent demand forecasting in a tertiary pediatric intensive care unit. J. Med. Syst. 40(10) (2016). https:\/\/doi.org\/10.1007\/s10916-016-0571-9","DOI":"10.1007\/s10916-016-0571-9"},{"key":"25_CR11","doi-asserted-by":"publisher","unstructured":"I\u0307mece, S., Beyca, \u00d6.F.: Demand forecasting with integration of time series and regression models in pharmaceutical industry. Int. J. Adv. Eng. Pure Sci. 34(3), 415\u2013425 (2022). https:\/\/doi.org\/10.7240\/jeps.1127844","DOI":"10.7240\/jeps.1127844"},{"key":"25_CR12","unstructured":"Sengupta, S., Dutta, R.: Identification of demand forecasting model considering key factors in the context of Healthcare products. Int. J. Appl. Innov. Eng. Manage. (2014)"},{"key":"25_CR13","doi-asserted-by":"publisher","unstructured":"Sudarshan, V.K., Brabrand, M., Range, T.M., Wiil, U.K.: Performance evaluation of Emergency Department patient arrivals forecasting models by including meteorological and calendar information: a comparative study. Comput. Biol. Med. 135 (2021). https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104541","DOI":"10.1016\/j.compbiomed.2021.104541"},{"issue":"2","key":"25_CR14","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1007\/s13198-019-00879-6","volume":"11","author":"MS Amalnick","year":"2020","unstructured":"Amalnick, M.S., Habibifar, N., Hamid, M., Bastan, M.: An intelligent algorithm for final product demand forecasting in pharmaceutical units. Int. J. Syst. Assur. Eng. Manage. 11(2), 481\u2013493 (2020). https:\/\/doi.org\/10.1007\/s13198-019-00879-6","journal-title":"Int. J. Syst. Assur. Eng. Manage."},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Armstrong, J.S., Green, K.C.: Demand forecasting: evidence-based methods. No. 24\/05. Monash University, Department of Econometrics and Business Statistics (2005). http:\/\/dx.doi.org\/10.2139\/ssrn.3063308","DOI":"10.2139\/ssrn.3063308"},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Archer, B.: Demand forecasting and estimation. SSRN Electr. J. 77\u201385 (1987)","DOI":"10.1201\/9781482275605-13"},{"issue":"3-4","key":"25_CR17","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/0169-2070(87)90031-8","volume":"3","author":"DJ Dalrymple","year":"1987","unstructured":"Dalrymple, D.J.: Sales forecasting practices: results from a United States survey. Int. J. Forecast. 3(3\u20134), 379\u2013391 (1987). https:\/\/doi.org\/10.1016\/0169-2070(87)90031-8","journal-title":"Int. J. Forecast."},{"issue":"3","key":"25_CR18","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/S0024-6301(76)80010-6","volume":"9","author":"P Doyle","year":"1976","unstructured":"Doyle, P., Fenwick, I.: Sales forecasting\u2014using a combination of approaches. Long Range Plan. 9(3), 60\u201364 (1976). https:\/\/doi.org\/10.1016\/S0024-6301(76)80010-6","journal-title":"Long Range Plan."},{"key":"25_CR19","unstructured":"Ali, J., et al.: Random forests and decision trees. Int. J. Comput. Sci. Issues (IJCSI) 9(5), 272 (2012)"},{"key":"25_CR20","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1007\/s12667-016-0203-y","volume":"8","author":"I Ghalehkhondabi","year":"2017","unstructured":"Ghalehkhondabi, I., Ardjmand, E., Weckman, G.R., et al.: An overview of energy demand forecasting methods published in 2005\u20132015. Energy Syst. 8, 411\u2013447 (2017). https:\/\/doi.org\/10.1007\/s12667-016-0203-y","journal-title":"Energy Syst."},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Ali, M.A., Matubber, M.L., Sharma, V., Balamurugan,B.: An improved and efficient technique for detecting Bengali fake news using machine learning algorithms. In: 2022 6th International Conference On Computing, Communication, Control And Automation (ICCUBEA), Pune, India, pp. 1-4. (2022). https:\/\/doi.org\/10.1109\/ICCUBEA54992.2022.10011096","DOI":"10.1109\/ICCUBEA54992.2022.10011096"},{"key":"25_CR22","unstructured":"https:\/\/www.kaggle.com\/datasets\/milanzdravkovic\/pharma-sales-data?select=salesmonthly.csv"}],"container-title":["Lecture Notes in Computer Science","Mining Intelligence and Knowledge Exploration"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44084-7_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T09:05:23Z","timestamp":1695459923000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44084-7_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031440830","9783031440847"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44084-7_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"24 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIKE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Mining Intelligence and Knowledge Exploration","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kristiansand","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Norway","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mike2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.mike.org.in\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"87","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}