Personalized type-II diabetes diagnosis in smart healthcare using a neutrosophic collaborative learning approach

Machine learning (ML) and deep learning (DL) technologies have proven to be highly effective in diagnosing type-II diabetes. Despite this, their primary focus tends to be on maximizing overall diagnostic accuracy. Consequently, there may still be inaccuracies in diagnosing individual patients.

A neutrosophic collaborative learning (NCL) approach is proposed in this study for type-II diabetes diagnosis. In this approach, multiple local artificial intelligence models (LAMs) work simultaneously to predict the likelihood of a patient having type-II diabetes. This allows for an efficient and comprehensive diagnosis from various perspectives. Each LAM utilizes a neutrosophic deep neural network (NDNN) to estimate the probability of the condition. The NDNN model considers factors like local learning inadequacy and prediction inaccuracy when evaluating the indeterminacy of predictions. If the indeterminacy membership exceeds a certain threshold, the prediction may not be trustworthy. The results from these predictions are then sent to a global server, where they are aggregated using a neutrosophic weighted intersection (NWI).

A benchmark case was employed to assess the effectiveness of the NCL approach in diagnosing type-II diabetes. The experimental results demonstrated that the NCL approach increased the hit rate for type-II diabetes diagnosis by an average of 9%. Furthermore, the collaboration of LAMs enhanced diagnostic accuracy by 5.6%.

Taking into account a variety of perspectives can enhance the effectiveness of diagnosing type-II diabetes. Converting diagnostic results into neutrosophic sets, which include three different memberships, also aids in evaluating the trustworthiness of each diagnostic outcome.