Radheya Suresh Afre
Veermata Jijabai Technological Institute, India
Posters-Accepted Abstracts: J Diabetes Metab
In the biomedical domain there are many problems which can be identified as prediction problem such as predicting the diabetic patient. Such prediction can be done with the help of periodic clinical data. Machine learning approaches for prediction such as Hidden Markov Model can be used effectively in solving real time problems. Hidden Markov Model (HMM) can be applied in many fields where the goal is to recover a data sequence that is not immediately observable and which is dependent on previous sequence. Thus, Hidden Markov Model can be used for detecting various diseases like diabetes, thyroid and heart problems. The prediction power of HMM can be useful to address many problems in medical domain. International Diabetes Federation (IDF) Risk Score is useful in calculating the risk of Diabetes. I am proposing the idea to apply HMM in predicting the risk of Diabetes disease over a period of time.
Email: radheya.afregmailcom
