nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

Artificial Intelligence in Health Care: Predictive Analysis on Diabetes Using Machine Learning Algorithms

verfasst von : Shruti Wadhwa, Karuna Babber

Erschienen in: Computational Science and Its Applications – ICCSA 2020

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Background: The healthcare organizations are producing heaps of data at alarming rate. This data comprises of medical records, genome-omics data, image scan or wearable medico device data that presents immense advantages and challenges at the same time. These ever growing challenges can be surpassed by applying effective artificial intelligence tools.

Methods: This paper uses the large volume of multimodal patient data to perform correlations between Body Mass Index, Blood Pressure, Glucose levels, Diabetes Pedigree Function and Skin Thickness of people in different age groups with diabetes. Python and data analytic packages are used to predict diabetes among people.

Results: The blood pressure count of diabetic people comes around 75–85 mmHg and sometimes even higher whereas it is in the range of 60–75 mmHg for non-diabetic people. The people with high body mass index and glucose levels of 120–200 mg/dl and more are found to be diabetic as against the lower body mass index with glucose levels of 85–105 mg/dl of normal people. The Diabetes Pedigree Function count of diabetic people has a peak at 0.25 whereas it is 0.125 in case of non-diabetic people. A similar slight difference in values of Age and Skin Thickness has been found for both diabetic and non-diabetic people.

Conclusion: Above results indicate a strong relationship between Blood Pressure, BMI and Glucose levels of people with diabetes whereas a moderate correlation has been found between Age, Skin Thickness and Diabetes Pedigree Function count of people with diabetes. Although present analysis attested many of the previous research findings but getting these inferences matched through analytical tools is a sole purpose of this paper.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Software Defect Prediction on Unlabelled Datasets: A Comparative Study

Nächstes Kapitel Learning to Classify Text Complexity for the Italian Language Using Support Vector Machines

Laney, D.: 3D Data Management: controlling data volume, velocity and variety, Application delivery strategies. META Group Inc., Stanford (2001)

Mauro, A.D., Greco, M., Grimaldi, M.: A formal definition of big data based on its essential features. Libr. Rev. 65(3), 122–135 (2016)CrossRef

Jacobs, A.: The pathologies of big data. Commun. ACM 52(8), 36–44 (2009)CrossRef

Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRef

Belle, A.: Big data analytics in healthcare. Biomed. Res. Int, E 2015, 370194 (2015)

Viceconti, M., Hunter, P., Hose, R.: Big data, big knowledge: big data for personalized healthcare. IEEE J. Biomed. Health Inf. 19, 1209–1215 (2015)CrossRef

Nasi, G., Cucciniello, M., Guerrazzi, C.: The role of mobile technologies in health care processes: the case of cancer supportive care. J. Med. Internet Res. 17(2), e26 (2015)CrossRef

Bali, J., Garg, R., Bali, R.T.: Artificial intelligence in healthcare and biomedical research: why a strong computational bioethics framework is required. Indian J. Ophthalmol. 67(1), 3–6 (2019). https://doi.org/10.4103/ijo.ijo_1292_18CrossRef

Doyle-Lindrud, S.: The evolution of electronic health record. Clin. J. Nurs. 19(2), 153–154 (2015)CrossRef

10.

Yin, Y.: The internet of things in healthcare an overview. J. Inf. Health Care 1, 3–13 (2016)

11.

Khare, A., Jeon, M., Sethi, I.K., Xu, B.: Machine learning theory and applications for healthcare. J. Healthcare Eng. (2017). https://doi.org/10.1155/2017/5263570

12.

Lamy, J.-B., Sekar, B., Guezennec, G., Bouaud, J., Seroussi, B.: Explainable artificial intelligence for breast cancer: a visual case-based reasoning approach. J. Artif. Intell. Med. 94, 42–53 (2019). https://doi.org/10.1016/j.artmed.2019.01.001CrossRef

13.

Lauritsen, S.M., et al.: Early detection of sepsis utilizing deep learning on electronic health record event sequences. J. Artif. Intell. 104 (2020). https://doi.org/10.1016/j.artmed.2020.101820

14.

Shameer, K.: Traditional bioinformatics in the era of real-time biomedical, health care and wellness data streams. Brief. Bioinform. 18(1), 105–124 (2017)CrossRef

15.

Reform. Thinking on its own: AI in the NHS (2018)

16.

Davenport, T., Kalakota, R.: The potential for artificial intelligence in healthcare. Future Healthcare J. 6(2), 94–98 (2019). https://doi.org/10.7861/futurehosp.6-2-94CrossRef

17.

Jiang, F., et al.: Artificial intelligence in healthcare: past, present and future. Stroke Vasc. Neurol. 0, e000101 (2017). https://doi.org/10.1136/svn-2017-000101

18.

Tran, B.X., et al.: Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. J. Clin. Med. 8, 360–369 (2019). https://doi.org/10.3390/jcm8030360CrossRef

19.

Moore, S.F.: Harnessing the power of intelligent machines to enhance primary care. J. General Pract. 68, 6–7 (2018)CrossRef

20.

Nesta. Confronting Dr. Robot creating a people-powered future for AI in health (2018)

21.

Reisman, M.: EHRs: the challenge of making electronic data usable and interoperable. J. Pharma Theor. 42(9), 572–575 (2017)

22.

De Moor, G., et al.: Using electronic health records for clinical research: the case of the EHR4CR project. J. Biomed. Inf. 53, 162–173 (2015). https://doi.org/10.1016/j.jbi.2014.10.006CrossRef

23.

Kaelber, D.C., Jha, A.K., Johnston, D., Middleton, B., Bates, D.W.: A research agenda for Personal Health Records (PHR). J. Am. Med. Inf. Assoc. 15(6), 729–736 (2008). https://doi.org/10.1197/jamia.m2547CrossRef

24.

Roehrs, A., da Costa, C.A.: Personal health records: a systematic literature review. Med. Internet Res. Nat. Libr. Med. 19(1), e13 (2017). https://doi.org/10.2196/jmir.5876

25.

https://www.kaggle.com/uciml/pima-indians-diabetes-database/

26.

https://pandas.pydata.org/

27.

Parker, E.: Python & ETL 2020: A list and comparison of the top python ETL tools (2020)

28.

https://numpy.org/

29.

https://matplotlib.org/

30.

https://scikit-learn.org/stable/

31.

William, C., Shiel Jr.: Medical definition of blood pressure. J. Medterms Med. Dic. (1998)

32.

Tsimihodimos, V., Clicerio, G.-V., Meigs, J.B., Ferrannini, E.: Hypertension and diabetes mellitus. J. Hypertens. 71, 422–428 (2018). https://doi.org/10.1161/HYPERTENSIONAHA.117.10546CrossRef

33.

Suastika, K., Dwipayana, P., Semandi, M.S., Kuswardhani, R.A.T.: Age is an important risk factor for type 2 Diabetes Mellitus and Cardiovascular Diseases. IntechOpen (2012). https://doi.org/10.5772/52397

34.

Kharroubi, A.T., Darwish, H.M.: Diabetes mellitus: the epidemic of the century. World J. Diabetes. 6(6), 850–867 (2015). https://doi.org/10.4239/wjd.v6.i6.850CrossRef

35.

Nuttall, F.Q.: Body mass index: obesity and health – a critical review. J. Nutr. Today 50(3), 117–128 (2015)CrossRef

36.

Mercaldo, F., Nardone, V., Santone, A.: Diabetes Mellitus affected patients classification and diagnosis through machine learning techniques. In: International Conference on Knowledge Based and Intelligent Information and Engineering Systems (2017). Procedia Computer Science, vol. 112, pp. 2519–2528

37.

Jose Derraik, G.B., Rademaker, M., Cutfield, S.W., Pinto, E.T.: Effects of age, gender and anatomical site on skin thickness in children and adults with diabetes. PLoS One 9(1) (2014). https://doi.org/10.1371/journal.pone.0086637

38.

Frankum, S., Ogden, J.: Estimation of blood glucose levels by people with diabetes: a cross-sectional study. Br. J. Gen. Pract. 55(521), 944–948 (2005)

39.

Biau, G.: Analysis of random forests model. J. Mach. Learn. Res. 13, 1063–1095 (2012)MathSciNetMATH

40.

Marijana, Z.-S., Sarlija, N., Has, A., Bilandzic, A.: Predicting company growth using logistic regression and neural networks, Cortian Oper. Res. Rev. (CRORR), 7, 229–248 (2016)

41.

Wu, D., Jennings, C., Terpenny, J., Gao, R.X., Kumara, S.: A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using Random Forests. J. Manuf. Sci. Eng. 139(7) (2017). https://doi.org/10.1115/1.4036350

Titel: Artificial Intelligence in Health Care: Predictive Analysis on Diabetes Using Machine Learning Algorithms
verfasst von: Shruti Wadhwa
Karuna Babber
Verlag: Springer International Publishing
Buch: Computational Science and Its Applications – ICCSA 2020
Print ISBN: 978-3-030-58801-4

Electronic ISBN: 978-3-030-58802-1

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-58802-1_26

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner