Breast cancer has been identified as the second leading cause of death among women worldwide after lung cancer and hence, it becomes extremely crucial to identify it at an early stage, which can considerably increase the chances of survival. The most important part in cancer detection is to be able to differentiate between benign and malignant tumors and this is where the work of Machine Learning comes in. Taking all the dependent features upon consideration, Supervised Machine Learning methods allow for classification with higher degree of accuracy and improve upon the misdiagnosis of the physicians, which might occur almost 20% of the time. In our paper, we are focusing towards understanding the shortcomings of digital mammograms in detection of breast cancer and utilize Machine Learning classifiers for the classification of benign and malignant tumors using image analysis. Apart from this, we are also looking into implementing Supervised Machine Learning classifiers such as Decision Tree, K Nearest Neighbour (KNN), Random Forest and Gaussian Naive Bayes classifiers for assessing the risks involved with breast cancer by analyzing the biomarkers that are involved with it. Our aim is to provide a comprehensive view on prediction of breast cancer through Machine Learning through both image and data analyses, which can play a pivotal role in prevention of misdiagnosis in future. Fig. 1. gives a layout for the breast cancer prediction using Supervised Machine learning classifiers.

The healthcare system in the Indian subcontinent is plagued with numerous issues related to the access, transfer, and storage of patient's medical records. The lack of infrastructure to properly communicate and track records between all key participants has allowed the distribution of counterfeit drugs, dependency on unsafe methods of communication, and lack of trust between patients and providers. During the global COVID-19 pandemic, the need for a robust communication and record tracking system has been further emphasized. To facilitate efficient communication and mitigate the mentioned issues, a nationwide EHR (electronic health record) system must be introduced to bring the healthcare system into digital space. To further enhance security, efficiency, and cost, the innovation of Blockchain is introduced. Blockchain is a decentralized data structure that allows secure transactions between untrusted parties without needing a central authority. In this paper, a Hyperledger fabric-based Blockchain Electronic Healthcare Record (EHR) system is proposed. The system is integrated with technologies such as NLP (Natural Language Processing), and Machine Learning to provide users with practical features.Povzetek: Predstavljen je elektronski zdravstveni zapis na osnovi bločnih in NLP tehnologij v kontekstu Indije.

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