Hereditary factor-based multi-featured algorithm for early diabetes detection using machine learning
Classification algorithm; Decision trees; Diabetes detection; Multi-featured algorithm; Prediction
Today's advent in the medical industry have given numerous chances to improve the quality of detection and reporting the diseases at the early stages for a better diagnosis. Modern day datasets generate fruitful information for timely and periodic monitoring of patients' health conditions. Such information is hidden to a naked eye or hidden in multiple track records of highly affected population. Diabetes mellitus is one such disease which is predominant among a global population which ultimately leads to blindness and death in some cases. The model proposed in this system attempts to design and deliver an intelligent solution for predicting diabetes in the early stages and address the problem of late detection and diagnosis. Intensive research is carried out in many tropical countries for automating this process through a machine learning model. The accuracy of machine learning algorithms is more than satisfactory in the detection of Type 2 diabetes from the dataset of PIMA Indians Diabetes Dataset. An additional feature of hereditary factor is implemented to the existing multiple objective fuzzy classifiers. The proposed model has improved the accuracy to 83% in the training and tested datasets when compared to NGSA model of prediction. 2022 Scrivener Publishing LLC.
Deepajothi S.; Juliana R.; Aruna S.K.; Thiagarajan R.
Artificial Intelligent Techniques for Wireless Communication and Networking, pp. 235-253.
https://doi.org/10.1002/9781119821809.ch15 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148085821&doi=10.1002%2f9781119821809.ch15&partnerID=40&md5=3da47231aabcdb719a732a2e49e6f8d5
Deepajothi S., Dept. of CSE, Gurunanak Institute of Technology, Hyderabad, India; Juliana R., Dept. of IT, Loyola ICAM College of Engineering and Technology, Chennai, India; Aruna S.K., Dept. of CSE, Christ (Deemed to be University) School of Engineering and Technology, Bangalore, India; Thiagarajan R., Dept. of CSE, Prathyusha Engineering College, Thiruvallur, India