Bionic Health-Care Innovation Using Artificial And Human Intelligence

Authors

  • Sheela D. V. Assistant Professor, School of Computer Science and Application, REVA University, Bangalore
  • Abhay Chaturvedi Associate Professor, Department of Electronics and Communication Engineering, GLA University, Mathura, Uttar Pradesh, 281406
  • Prajakta Naregalkar Assistant professor, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune
  • M. Chiranjivi Associate Professor, Department of EEE, Hyderabad Institute of Technology and Management, Telangana -501401
  • Chetan More Assistant Professor, Department of E&TC, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune
  • Namita K. Shinde Assistant Professor, Department of E&TC, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune
  • Anurag Shrivastava Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu, India

Keywords:

Artificial intelligence, wearable devices, healthcare, point-of-care sensors, smart sensors are some of the areas of focus in this industry.

Abstract

According to the findings of a pilot investigation, women who have type 2 diabetes (T2DM) have an increased chance of developing breast cancer. In this study, we compared the accuracy of three different models for determining the likelihood of developing breast cancer in type 2 diabetics who had different characteristics at the outset. The information was included into a model with the goal of determining whether or not a patient with type 2 diabetes also had a higher risk of acquiring breast cancer. In addition, we conducted research to identify potential breast cancer risk factors in order to incorporate their influence into our study and take appropriate action. Researchers have turned to a method known as synthetic minority oversampling in order to gather more information from samples of communities who are underrepresented in their studies.  The ratio of training data to test data was close to 39 to 1 for each question in the survey. The effectiveness of the Logistic Regression (LR), Artificial Neural Network (ANN), and Random Forest (RF) models were evaluated using metrics such as recall, accuracy, F1 score, and area under the receiver operating characteristic curve (AUC), respectively. The area under the curve (AUC) for each of the three models was comparable (0.83 for LR, 0.865 for ANN, and 0.959 for RF). The RF model has the greatest AUC out of the three that were taken into consideration. When it came to forecasting whether or not T2DM patients will get breast cancer, the RF model performed the best out of the LR, ANN, and RF models.

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Published

12.07.2023

How to Cite

D. V., S. ., Chaturvedi, A. ., Naregalkar, P. ., Chiranjivi, M. ., More, C. ., Shinde, N. K. ., & Shrivastava, A. . (2023). Bionic Health-Care Innovation Using Artificial And Human Intelligence. International Journal of Intelligent Systems and Applications in Engineering, 11(9s), 225–232. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3111

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Research Article

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