Deep learning framework for analysis of health factors in internet-of-medical things


  • Syed Hauider Abbas Integral university, Lucknow, India
  • Ramakrishna Kolikipogu Chaitanya Bharathi Institute of Technology, Hyderabad, India
  • Vuyyuru Lakshma Reddy Koneru Lakshmaiah Education Foundation, Vaddeswaram, India
  • Jnaneshwar Pai Maroor NMAM Institute of Technology-Affiliated to NITTE (Deemed to be University), India
  • Deepak Kumar Manav Rachna International Institute of Research and Studies, Faridabad (Haryana), India
  • Mangal Singh Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India



Internet-of-Medical-Things, CNN, Health Pattern Discovery, Deep Learning, Healthcare


The introduction of IoT technologies, such as those used in remote health monitoring applications, has revolutionized conventional medical care. Furthermore, the approach utilized to obtain insights from the scrutiny of lifestyle elements and activities is crucial to the success of tailored healthcare and disease prevention services. Intelligent data retrieval and classification algorithms allow for the investigation of disease and the prediction of aberrant health states. The Convolutional-neural-network(CNN) strategy is utilized to forecast such anomaly because it can successfully recognize the knowledge significant to disease anticipation from amorphous medical heath records. Conversely, if a fully coupled network-topology is used, CNN guzzles a huge memory. Furthermore, the complexity analysis of the model may rise as the number of layers grows. Therefore, we present a CNN target recognition and anticipation strategy based on the Pearson-Correlation-Coefficient(PCC) and standard pattern activities to address these shortcomings of the CNN-model. It is built in this framework and used for classification purposes. In the initial hidden layer, the most crucial health-related factors are chosen, and in the next, a correlation-coefficient examination is performed to categorize the health factors into positively &negatively correlated groups. Mining the occurrence of regular patterns among the categorized health parameters also reveals the behaviors of regular patterns. The model's output is broken down into obesity, hypertension, and diabetes-related factors with known correlations. To lessen the impact of the CNN-typical knowledge discovery paradigm, we use two separate datasets. The experimental results reveal that the proposed model outperforms three other machine learning techniques while requiring less computational effort.

Author Biographies

Syed Hauider Abbas, Integral university, Lucknow

Assistant professor of Department of Computer science& Engineering

Ramakrishna Kolikipogu, Chaitanya Bharathi Institute of Technology, Hyderabad

Professor, Department of Information Technology,

Vuyyuru Lakshma Reddy, Koneru Lakshmaiah Education Foundation, Vaddeswaram

Assistant Professor, Department of CSE

Jnaneshwar Pai Maroor, NMAM Institute of Technology-Affiliated to NITTE (Deemed to be University)

Assistant Professor Department of Humanities

Deepak Kumar, Manav Rachna International Institute of Research and Studies, Faridabad (Haryana)

Department of Applied sciences

Mangal Singh, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune

Department of Electronics and Telecommunication Engg.


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Special Issue 2023 - 6G System Technologies