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· One min read
Dear Colleagues,

We are pleased to invite you to submit your works to our Special Issue. In this Special Issue, we aim to cover recent advances in artificial intelligence (AI) for healthcare with a sustainability perspective in mind, from both academic researchers and industry developers. Any type of article aligned with the journal (original research, case study, technical report, short communication, and reviews) is welcome for this Special Issue. Topics of interests include, but are not limited to, the following:
  • Health informatics
  • Artificial intelligence in healthcare
  • Personalized healthcare
  • Clinical decision support systems
  • IoT and big data in healthcare
  • Machine learning and deep learning in healthcare
  • Descriptive, diagnostic, predictive analytics in healthcare
  • Data security and privacy in healthcare
  • etc.

  • Please consider contributing to this Special Issue. Thank you for your consideration.

    Dr. Muhammad Syafrudin
    Dr. Ganjar Alfian
    Prof. Dr. Muhammad Anshari
    Assoc. Prof. Dr. Tony Hadibarata
    Guest Editors

    Deadline: 30 September 2021
    Submission link:

    · 2 min read


    As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input risk-factors from individuals. The proposed HPM consists of Density-based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection to remove the outlier data, Synthetic Minority Over-Sampling Technique (SMOTE) to balance the distribution of class, and Random Forest (RF) to classify the diseases. Three benchmark datasets were utilized to predict the risk of diabetes and hypertension at the initial stage. The result showed that by integrating DBSCAN-based outlier detection, SMOTE, and RF, diabetes and hypertension could be successfully predicted. The proposed HPM provided the best performance result as compared to other models for predicting diabetes as well as hypertension. Furthermore, our study has demonstrated that the proposed HPM can be applied in real cases in the IoT-based Health-care Monitoring System, so that the input risk-factors from end-user android application can be stored and analyzed in a secure remote server. The prediction result from the proposed HPM can be accessed by users through an Android application; thus, it is expected to provide an effective way to find the risk of diabetes and hypertension at the initial stage.

    Published in: Applied Sciences
    DOI: 10.3390/app8081325