Integrating Blockchain and Machine Learning for Healthcare Systems

Abstract

Health care systems are that systems which help the patients reach directly the concerned specialist. These are the systems which are having lots of advantages during the pandemic situation and in high emergency situations.In the proposed work user will search for the disease summary (disease and treatment related information) by giving symptoms as a query in the search engine. Initially when a pdf is downloaded and saved in the system it first performs per processing on the data in the document and the extracted relevant data is stored in the database. The symptoms entered by the user are further classified using SVM classifier to make the further process easier to find the semantic keyword which helps to identify the disease easily and quickly. Then the semantic keyword found is matched with the stored medical input database to identify the exact disease related to that keyword present. Once the disease related to the symptom is identified, it is sent to medical database to extract the articles pertaining to that disease. The preprocessing process involves tokenization, removal of stop words and stemming. Followed by that, relevant information is extracted using the keyword searching algorithm. In our implementation of our proposed system, we have used SVM classifier which gives us an improved result. The decentralized database where transactions get recorded in append only shared ledger has many advantages in healthcare industry. In medical treatment, the complete history of patient is very important and value is added when same information is accessed by different parties. The convergence of these two technologies can give highly accurate results in terms of machine learning with the security and reliability of Blockchain Technology. Keywords: Machine Learning, Tokenization, Stemming, SVM classifier, Naïve Bayes, Decision Trees, Blockchain.