My colleagues and I are guest editing a special issue “Applied Artificial Intelligence for Sustainability”, which will be published continuously by Sustainability (SSCI SCIE JCR Q2, Scopus SJR Q1).
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:
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
Deadline: 30 September 2021
Submission link: mdpi.com/si/58663
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