Conference Paper2019Cited by 20

An Optimization Approach to Improve Classification Performance in Cancer and Diabetes Prediction

Mustakim Al Helal, Atiqul Islam Chowdhury, Ashraful Islam, Eshtiak Ahmed, Md Swakshar Mahmud, Sabrina Hossain

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE)

IEEE, pp. 1–5

  • Good Health and Well-being — Relevance: 81%
DOI: 10.1109/ecace.2019.8679413URL

Abstract

There are many destructive diseases in the world which cause rapid death by taking time to affect such as cancer and diabetes. They take a lot of time to spread, thus they are curable or somewhat scalable to a great extent if they are diagnosed soon after introduced into the human body. Research shows that almost all type of cancer can be cured if they are detected in the early stage. It is also true for diabetes as they can be controlled if they are detected at the right time. So, a prediction technique that takes help from the computer and processes data from affected user to detect possible contamination can be a great tool for assisting both the doctors and patients with these diseases. A challenge in the process is that the detection accuracy has to be acceptable in order to make the system a reliable one. In this study, we have analyzed medical data using several classification algorithms in order to optimize classifier performance for cancer and diabetes prediction.

Keywords

  • diabetes prediction
  • cancer prediction
  • medical data analysis
  • early disease detection
  • disease classification
  • machine learning classifiers

CCDS Authors

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

Eshtiak Ahmed
Eshtiak Ahmed

Eshtiak Ahmed

  • Collaborator, HCI Wing

Doctoral Researcher

Gamification Group

Tampere University, Finland

Published at

UbiComp, ICIP, HCII, IEEE Access

Research Interests

Human-Computer Interaction, Human-Robot Interaction, Gamification and Assistive Technologies

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Cited By

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