{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T06:06:21Z","timestamp":1774418781814,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":20,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,3,28]],"date-time":"2019-03-28T00:00:00Z","timestamp":1553731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,3,28]]},"DOI":"10.1145\/3326172.3326213","type":"proceedings-article","created":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T19:23:35Z","timestamp":1562009015000},"page":"253-259","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Predictive Analytics in Healthcare for Diabetes Prediction"],"prefix":"10.1145","author":[{"given":"Faizan","family":"Zafar","sequence":"first","affiliation":[{"name":"School of EE &amp; CS, National University of Sciences &amp; Technology, Islamabad, Pakistan"}]},{"given":"Saad","family":"Raza","sequence":"additional","affiliation":[{"name":"School of EE &amp; CS, National University of Sciences &amp; Technology, Islamabad, Pakistan"}]},{"given":"Muhammad Umair","family":"Khalid","sequence":"additional","affiliation":[{"name":"School of EE &amp; CS, National University of Sciences &amp; Technology, Islamabad, Pakistan"}]},{"given":"Muhammad Ali","family":"Tahir","sequence":"additional","affiliation":[{"name":"School of EE &amp; CS, National University of Sciences &amp; Technology, Islamabad, Pakistan"}]}],"member":"320","published-online":{"date-parts":[[2019,3,28]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for","author":"Choa N.H.","year":"2045","unstructured":"N.H. Choa , J.E. Shaw , S. Karuranga , Y. Huang , J.D. da Rocha Fernandes , A.W. Ohlrogge , B. Malanda . 2018. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045 . Diabetes Research and Clinical Practice Volume 138, April 2018, Pages 271--281 N.H. Choa, J.E. Shaw, S. Karuranga, Y. Huang, J.D. da Rocha Fernandes, A.W. Ohlrogge, B. Malanda. 2018. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Research and Clinical Practice Volume 138, April 2018, Pages 271--281"},{"key":"e_1_3_2_1_2_1","unstructured":"SA Meo & Zia Inam & Bukhari Ishfaq & Arain Shoukat. 2016. Type 2 diabetes mellitus in Pakistan: Current prevalence and future forecast. JPMA. The Journal of the Pakistan Medical Association. 66. 1637--1642.  SA Meo & Zia Inam & Bukhari Ishfaq & Arain Shoukat. 2016. Type 2 diabetes mellitus in Pakistan: Current prevalence and future forecast. JPMA. The Journal of the Pakistan Medical Association. 66. 1637--1642."},{"key":"e_1_3_2_1_3_1","volume-title":"Diabetes Numeracy and Blood Glucose Control: Association With Type of Diabetes and Source of Care. Clinical Diabetes","author":"Zaugg Stephanie D.","year":"2014","unstructured":"Stephanie D. Zaugg , Godwin Dogbey , Karen Collins , Sharon Reynolds , Carter Batista , Grace Brannan , Jay H. Shubrook . 2014. Diabetes Numeracy and Blood Glucose Control: Association With Type of Diabetes and Source of Care. Clinical Diabetes Oct 2014 , 32 (4) 152--157. Stephanie D. Zaugg, Godwin Dogbey, Karen Collins, Sharon Reynolds, Carter Batista, Grace Brannan, Jay H. Shubrook. 2014. Diabetes Numeracy and Blood Glucose Control: Association With Type of Diabetes and Source of Care. Clinical Diabetes Oct 2014, 32 (4) 152--157."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5121\/ijdkp.2015.5101"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Andreas Mayr Harald Binder Olaf Gefeller Matthias Schmid. 2014. The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling. Methods Inf Med 2014; 53(6): 419--427.  Andreas Mayr Harald Binder Olaf Gefeller Matthias Schmid. 2014. The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling. Methods Inf Med 2014; 53(6): 419--427.","DOI":"10.3414\/ME13-01-0122"},{"key":"e_1_3_2_1_6_1","first-page":"396","volume-title":"2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox's Bazar","author":"Hashi E. K.","unstructured":"E. K. Hashi , M. S. U. Zaman and M. R. Hasan . 2017. An expert clinical decision support system to predict disease using classification techniques . 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox's Bazar , pp. 396 -- 400 . E. K. Hashi, M. S. U. Zaman and M. R. Hasan. 2017. An expert clinical decision support system to predict disease using classification techniques. 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox's Bazar, pp. 396--400."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0179805"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Kavakiotis Ioannis and Tsave Olga and Salifoglou Athanasios and Maglaveras N and Vlahavas I and Chouvarda Ioanna. 2017. Machine Learning and Data Mining Methods in Diabetes Research. Computational and Structural Biotechnology Journal. 15.  Kavakiotis Ioannis and Tsave Olga and Salifoglou Athanasios and Maglaveras N and Vlahavas I and Chouvarda Ioanna. 2017. Machine Learning and Data Mining Methods in Diabetes Research. Computational and Structural Biotechnology Journal. 15.","DOI":"10.1016\/j.csbj.2016.12.005"},{"key":"e_1_3_2_1_9_1","first-page":"619","volume-title":"International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","author":"Kalyankar G. D.","unstructured":"G. D. Kalyankar , S. R. Poojara and N. V. Dharwadkar . 2017. Predictive analysis of diabetic patient data using machine learning and Hadoop . International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) , Palladam , pp. 619 -- 624 . G. D. Kalyankar, S. R. Poojara and N. V. Dharwadkar. 2017. Predictive analysis of diabetic patient data using machine learning and Hadoop. International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, pp. 619--624."},{"key":"e_1_3_2_1_10_1","first-page":"673","volume-title":"1st International Conference on Next Generation Computing Technologies (NGCT)","author":"Anand A.","unstructured":"A. Anand and D. Shakti . 2015. Prediction of diabetes based on personal lifestyle indicators . 1st International Conference on Next Generation Computing Technologies (NGCT) , Dehradun , pp. 673 -- 676 . A. Anand and D. Shakti. 2015. Prediction of diabetes based on personal lifestyle indicators. 1st International Conference on Next Generation Computing Technologies (NGCT), Dehradun, pp. 673--676."},{"key":"e_1_3_2_1_11_1","volume-title":"UCI Machine Learning Repository","author":"Karra Taniskidou D.","unstructured":"Dua, D. and Karra Taniskidou , E. 2017. UCI Machine Learning Repository . Irvine, CA : University of California , School of Information and Computer Science. DOI= http:\/\/archive.ics.uci.edu\/ml. Dua, D. and Karra Taniskidou, E. 2017. UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science. DOI= http:\/\/archive.ics.uci.edu\/ml."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.2337\/db15-0033"},{"key":"e_1_3_2_1_13_1","first-page":"501","volume-title":"18th Iranian Conference on Electrical Engineering","author":"Fathi Ganji M.","year":"2010","unstructured":"M. Fathi Ganji and M. Saniee Abadeh . 2010. Using fuzzy ant colony optimization for diagnosis of diabetes disease . 18th Iranian Conference on Electrical Engineering , Isfahan , 2010 , pp. 501 -- 505 . M. Fathi Ganji and M. Saniee Abadeh. 2010. Using fuzzy ant colony optimization for diagnosis of diabetes disease. 18th Iranian Conference on Electrical Engineering, Isfahan, 2010, pp. 501--505."},{"key":"e_1_3_2_1_14_1","volume-title":"Antonino.","author":"Linder Ehab","year":"2002","unstructured":"Mohamed, Ehab and Linder , Roland and Perriello , Gabriele and Daniele , Nicola and P\u00f6ppl , Siegfried and De Lorenzo , Antonino. 2002 . Predicting Type 2 diabetes using an electronic nose-based artificial neural network analysis. Diabetes , nutrition & metabolism. 15. 215--21. Mohamed, Ehab and Linder, Roland and Perriello, Gabriele and Daniele, Nicola and P\u00f6ppl, Siegfried and De Lorenzo, Antonino. 2002. Predicting Type 2 diabetes using an electronic nose-based artificial neural network analysis. Diabetes, nutrition & metabolism. 15. 215--21."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"R Chandvaniya Jitendra and Aluvalu Rajanikanth. 2014. Ranking with Distance based Outlier Detection Techniques: A Survey. International Journal of Computer Applications. 89.  R Chandvaniya Jitendra and Aluvalu Rajanikanth. 2014. Ranking with Distance based Outlier Detection Techniques: A Survey. International Journal of Computer Applications. 89.","DOI":"10.5120\/15505-4207"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"S. Vijayarani and S. Dhayanand. 2015. Data Mining Classification Algorithms for Kidney Disease Prediction. International Journal on Cybernetics & Informatics. 4. 13--25.  S. Vijayarani and S. Dhayanand. 2015. Data Mining Classification Algorithms for Kidney Disease Prediction. International Journal on Cybernetics & Informatics. 4. 13--25.","DOI":"10.5121\/ijci.2015.4402"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSDE.2010.58"},{"key":"e_1_3_2_1_18_1","volume-title":"Data Mining: Concepts, models and techniques","year":"2011","unstructured":"Gorunescu, Florin. 2011 . Data Mining: Concepts, models and techniques . Intelligent Systems Reference Library. Berlin Heidelberg, Springer-Verlag . p. 256--60 Gorunescu, Florin. 2011. Data Mining: Concepts, models and techniques. Intelligent Systems Reference Library. Berlin Heidelberg, Springer-Verlag. p. 256--60"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Nirmala Devi M. Balamurugan A. & Reshma Kris M. 2016. Developing a Modified Logistic Regression Model for Diabetes Mellitus and Identifying the Important Factors of Type II Dm. Indian Journal Of Science And Technology 9(4).  Nirmala Devi M. Balamurugan A. & Reshma Kris M. 2016. Developing a Modified Logistic Regression Model for Diabetes Mellitus and Identifying the Important Factors of Type II Dm. Indian Journal Of Science And Technology 9(4).","DOI":"10.17485\/ijst\/2016\/v9i4\/87028"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Shankaracharya and Odedra Devang and Mallick Medhavi and Shukla Prateek and Samanta Subir and Vidyarthi Ambarish. 2011. Java-Based Diabetes Type 2 Prediction Tool for Better Diagnosis. Diabetes technology & therapeutics. 14. 251--6.  Shankaracharya and Odedra Devang and Mallick Medhavi and Shukla Prateek and Samanta Subir and Vidyarthi Ambarish. 2011. Java-Based Diabetes Type 2 Prediction Tool for Better Diagnosis. Diabetes technology & therapeutics. 14. 251--6.","DOI":"10.1089\/dia.2011.0202"}],"event":{"name":"ICBET ' 19: 2019 9th International Conference on Biomedical Engineering and Technology","location":"Tokyo Japan","acronym":"ICBET ' 19"},"container-title":["Proceedings of the 2019 9th International Conference on Biomedical Engineering and Technology"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3326172.3326213","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3326172.3326213","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:25:59Z","timestamp":1750206359000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3326172.3326213"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,28]]},"references-count":20,"alternative-id":["10.1145\/3326172.3326213","10.1145\/3326172"],"URL":"https:\/\/doi.org\/10.1145\/3326172.3326213","relation":{},"subject":[],"published":{"date-parts":[[2019,3,28]]},"assertion":[{"value":"2019-03-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}