Skip to main content
Top

2019 | OriginalPaper | Chapter

Detection and Analysis of Life Style based Diseases in Early Phase of Life: A Survey

Authors : Pankaj Ramakant Kunekar, Mukesh Gupta, Basant Agarwal

Published in: Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics

Publisher: Springer Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In India there is big transition in life style due to industrialization and western influence. Life style diseases are on surging rate with it affect across all age borders. According to a recent health survey almost 60% of all death reported in India are due to life style and non-communicable diseases (NCD) with life style contributing the major part in it. Early screening and predictive analysis is way forward to put a break on surging life style diseases. In this work a survey on scalable technologies assisting for early screening and predictive analysis for life style diseases is done. Each of technologies is analyzed in perceptive of multiple parameters like effectiveness, cost, convenience, adaptability rate etc. and open areas identified for further research.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

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!

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!

Literature
1.
go back to reference Indian Council of Medical Research (ICMR): India State-Level Disease Burden Study report (2017) Indian Council of Medical Research (ICMR): India State-Level Disease Burden Study report (2017)
2.
go back to reference Chen, M.: Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5, 8869–8879 (2016)CrossRef Chen, M.: Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5, 8869–8879 (2016)CrossRef
3.
go back to reference Ravi, D., Wong, C.: Deep learning for health informatics. IEEE J. Biomed. Health Inf. 21(1), 4–21 (2017)CrossRef Ravi, D., Wong, C.: Deep learning for health informatics. IEEE J. Biomed. Health Inf. 21(1), 4–21 (2017)CrossRef
4.
go back to reference Nguyen, A, Yosinski J, Clune J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: Computer Vision and Pattern Recognition (CVPR 2015). IEEE (2015) Nguyen, A, Yosinski J, Clune J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: Computer Vision and Pattern Recognition (CVPR 2015). IEEE (2015)
5.
go back to reference Kuriyan, J., Cobb, N.: Forecasts of cancer and chronic patients: big data metrics of population health, Cornell University library, pp. 1–26 (2013) Kuriyan, J., Cobb, N.: Forecasts of cancer and chronic patients: big data metrics of population health, Cornell University library, pp. 1–26 (2013)
6.
go back to reference Wang, A.C.A.: Big data analytics as applied to diabetes management. Eur. J. Clin. Biomed. Sci. 2(5), 29–38 (2016) Wang, A.C.A.: Big data analytics as applied to diabetes management. Eur. J. Clin. Biomed. Sci. 2(5), 29–38 (2016)
7.
go back to reference Razavian, N., Blecker, S., Schmidt, A.M.: Population-level prediction of type 2 diabetes from claims data and analysis of risk factors. Big Data 3(4), 277–282 (2015)CrossRef Razavian, N., Blecker, S., Schmidt, A.M.: Population-level prediction of type 2 diabetes from claims data and analysis of risk factors. Big Data 3(4), 277–282 (2015)CrossRef
8.
go back to reference Dey, S., Pahwa, P.: Prakriti and its associations with metabolism, chronic diseases, and genotypes: possibilities of new born screening and a lifetime of personalized prevention. J. Ayurveda Integr. Med. 5(1), 15 (2014)CrossRef Dey, S., Pahwa, P.: Prakriti and its associations with metabolism, chronic diseases, and genotypes: possibilities of new born screening and a lifetime of personalized prevention. J. Ayurveda Integr. Med. 5(1), 15 (2014)CrossRef
9.
go back to reference Christensen, T., Frandsen, A.: Machine learning methods for disease prediction with claims data. In: IEEE International Conference on Healthcare Informatics (2018) Christensen, T., Frandsen, A.: Machine learning methods for disease prediction with claims data. In: IEEE International Conference on Healthcare Informatics (2018)
10.
go back to reference Pennington, J., Socher, R., Manning, C,D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP) (2014) Pennington, J., Socher, R., Manning, C,D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP) (2014)
11.
go back to reference dos Santos, H.D., Ana Helena, D.P.S., Ulbrich, A.H.: DDC-outlier: preventing medication errors using unsupervised learning. J. Med. 14(8), 874–881 (2018) dos Santos, H.D., Ana Helena, D.P.S., Ulbrich, A.H.: DDC-outlier: preventing medication errors using unsupervised learning. J. Med. 14(8), 874–881 (2018)
12.
go back to reference Ma, Y., Wang, Y., Yang, J.: Big health application system based on health Internet of Things and big data. Special Section on Healthcare Big Data, October 2016 Ma, Y., Wang, Y., Yang, J.: Big health application system based on health Internet of Things and big data. Special Section on Healthcare Big Data, October 2016
13.
go back to reference Nahla, H., Barakat, M.N., Bradley, A.P.: Intelligible support vector machines for diagnosis of diabetes mellitus. IEEE Trans. Inf Technol. Biomed. 14(4), 1114–1120 (2010)CrossRef Nahla, H., Barakat, M.N., Bradley, A.P.: Intelligible support vector machines for diagnosis of diabetes mellitus. IEEE Trans. Inf Technol. Biomed. 14(4), 1114–1120 (2010)CrossRef
14.
go back to reference Escudero, J., Ifeachor, E.: Machine learning-based method for personalized and cost-effective detection of Alzheimer’s disease. IEEE Trans. Biomed. Eng. 60(1), 164–168 (2013)CrossRef Escudero, J., Ifeachor, E.: Machine learning-based method for personalized and cost-effective detection of Alzheimer’s disease. IEEE Trans. Biomed. Eng. 60(1), 164–168 (2013)CrossRef
15.
go back to reference Esteva, A., Kuprel, B.: Dermatologist-level classification of skin cancer with deep neural networks. Letter Macmillan Publishers Limited, part of Springer Nature (2017) Esteva, A., Kuprel, B.: Dermatologist-level classification of skin cancer with deep neural networks. Letter Macmillan Publishers Limited, part of Springer Nature (2017)
16.
go back to reference Luo, G.: PredicT-ML: a tool for automating machine learning model building with big clinical data. Health Inf. Sci. Syst. 4(1), 5 (2016)CrossRef Luo, G.: PredicT-ML: a tool for automating machine learning model building with big clinical data. Health Inf. Sci. Syst. 4(1), 5 (2016)CrossRef
17.
go back to reference Du, W.: A feature selection method based on multiple kernel learning with expression profiles of different types. Biodata Min. 10(1), 4 (2017)CrossRef Du, W.: A feature selection method based on multiple kernel learning with expression profiles of different types. Biodata Min. 10(1), 4 (2017)CrossRef
18.
go back to reference Pham, T., Tran, T., Phung, D., Venkatesh, S.: Predicting healthcare trajectories from medical records: a deep learning approach. J. Biomed. Inf. 69, 218–229 (2017)CrossRef Pham, T., Tran, T., Phung, D., Venkatesh, S.: Predicting healthcare trajectories from medical records: a deep learning approach. J. Biomed. Inf. 69, 218–229 (2017)CrossRef
19.
go back to reference Miotto, R., Li, L.: Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6, 26094 (2016)CrossRef Miotto, R., Li, L.: Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6, 26094 (2016)CrossRef
20.
go back to reference Zhang, D., Zou, L.: Integrating feature selection and feature extraction methods with deep learning to predict clinical outcome of breast cancer. IEEE, March 2018CrossRef Zhang, D., Zou, L.: Integrating feature selection and feature extraction methods with deep learning to predict clinical outcome of breast cancer. IEEE, March 2018CrossRef
21.
go back to reference Enshaeifar, S., Zoha, A., Markides, A.: Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques. PLoS ONE 13(5), e0195605 (2018)CrossRef Enshaeifar, S., Zoha, A., Markides, A.: Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques. PLoS ONE 13(5), e0195605 (2018)CrossRef
22.
go back to reference Zhong, H., Xiao, J.: Enhancing health risk prediction with deep learning on big data and revised fusion node paradigm. Sci. Prog. 2017, 18 (2017) Zhong, H., Xiao, J.: Enhancing health risk prediction with deep learning on big data and revised fusion node paradigm. Sci. Prog. 2017, 18 (2017)
23.
go back to reference Nair, L.R., Shetty, S.D.: Applying spark based machine learning model on streaming big data for health status prediction. Elsevier, March 2017 Nair, L.R., Shetty, S.D.: Applying spark based machine learning model on streaming big data for health status prediction. Elsevier, March 2017
24.
go back to reference Han, L., Luo, S., Yu, J., Pan, L., Chen, S.: Rule extraction from support vector machines using ensemble learning approach: an application for diagnosis of diabetes. IEEE J. Biomed. Health Inform. 19, 728–734 (2015)CrossRef Han, L., Luo, S., Yu, J., Pan, L., Chen, S.: Rule extraction from support vector machines using ensemble learning approach: an application for diagnosis of diabetes. IEEE J. Biomed. Health Inform. 19, 728–734 (2015)CrossRef
25.
go back to reference Lee, B.J., Ku, B., Nam, J., Pham, D.D., Kim, J.Y.: Prediction of fasting plasma glucose status using anthropometric measures for diagnosing type 2 diabetes. IEEE J. Biomed. Health Inform. 18, 555–561 (2014)CrossRef Lee, B.J., Ku, B., Nam, J., Pham, D.D., Kim, J.Y.: Prediction of fasting plasma glucose status using anthropometric measures for diagnosing type 2 diabetes. IEEE J. Biomed. Health Inform. 18, 555–561 (2014)CrossRef
26.
go back to reference Ling, S.H., San, P.P., Nguyen, H.T.: Non-invasive hypoglycemia monitoring system using extreme learning machine for type 1 diabetes. ISA Trans. 64, 440–446 (2016)CrossRef Ling, S.H., San, P.P., Nguyen, H.T.: Non-invasive hypoglycemia monitoring system using extreme learning machine for type 1 diabetes. ISA Trans. 64, 440–446 (2016)CrossRef
27.
go back to reference Li, C.M., et al.: Synchronizing chaotification with support vector machine and wolf pack search algorithm for estimation of peripheral vascular occlusion in diabetes mellitus. Biomed. Signal Process. Control 9, 45–55 (2014)CrossRef Li, C.M., et al.: Synchronizing chaotification with support vector machine and wolf pack search algorithm for estimation of peripheral vascular occlusion in diabetes mellitus. Biomed. Signal Process. Control 9, 45–55 (2014)CrossRef
28.
go back to reference Tripathy, R.K., Sharma, L.N., Dandapat, S.: A new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification. Healthcare Technol. Lett. 1, 98–103 (2014)CrossRef Tripathy, R.K., Sharma, L.N., Dandapat, S.: A new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification. Healthcare Technol. Lett. 1, 98–103 (2014)CrossRef
29.
go back to reference Oster, J., Behar, J., Sayadi, O., Nemati, S., Johnson, A.E., Clifford, G.D.: Semisupervised ECG ventricular beat classification with novelty detection based on switching Kalman filters. IEEE Trans. Biomed. Eng. 62(9), 2125–2134 (2015)CrossRef Oster, J., Behar, J., Sayadi, O., Nemati, S., Johnson, A.E., Clifford, G.D.: Semisupervised ECG ventricular beat classification with novelty detection based on switching Kalman filters. IEEE Trans. Biomed. Eng. 62(9), 2125–2134 (2015)CrossRef
30.
go back to reference Montañez, C.A.C.: Machine learning approaches for the prediction of obesity using publicly available genetic profiles. In: International Joint Conference on Neural Networks (IJCNN) (2017) Montañez, C.A.C.: Machine learning approaches for the prediction of obesity using publicly available genetic profiles. In: International Joint Conference on Neural Networks (IJCNN) (2017)
31.
go back to reference Singh, R., Gahlot, A., Mittal, M.: IoT based intelligent robot for various disasters monitoring and prevention with visual data manipulating. Int. J. Tomogr. Simul. 32(1), 89–99 (2019) Singh, R., Gahlot, A., Mittal, M.: IoT based intelligent robot for various disasters monitoring and prevention with visual data manipulating. Int. J. Tomogr. Simul. 32(1), 89–99 (2019)
Metadata
Title
Detection and Analysis of Life Style based Diseases in Early Phase of Life: A Survey
Authors
Pankaj Ramakant Kunekar
Mukesh Gupta
Basant Agarwal
Copyright Year
2019
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-13-8300-7_6

Premium Partner