Effective Model for Improving Symptoms-Based Disease Prediction by BiMM - BERT Algorithm

Nowadays, symptoms-based disease prediction is more helpful for predicting the correct type of disease. These predictions are more helpful to the medical industry and doctors; they can reduce the time taken to check up and accurately predict the disease type. Combining the Bidirectional Encoder Representations from Transformers (BERT), Natural Language Processing (NLP), Hidden Markov Model (HMM) and Bi-directional Maximal Matching algorithm (BiMM), a unique word classification algorithm, BERT-BiMM, is presented. It can use quick word segmentation to match sub-dictionaries. The knowledge hidden in many research topics can be found using Natural Language Processing methods. The knowledge hidden in many research topics can be found using NLP methods. The remaining text is joined by the remaining specific words BiMM post text segmentation; the remaining text is connected by the remaining single words using Hidden Markov Model. We compared the real-world clinical text. We took some symptoms of the disease. HMM, BiMM, achieves maximum accuracy. BERT is used to connect the remaining text with these methods, and high accuracy in medical text segmentations will be obtained if this model outperforms provincial models by achieving strict scores of 91.29% on the BiMM model.