Open Source Robust Machine Learning Software for Medical Patient Data Analysis and Cloud Storage

Big data and artificial intelligence-based researches in the health care arena have radically changed the sector with better preventive health care, early diagnosis of diseases, and advanced assistive technology along with numerous other areas. Health care facilities, academic research centers, and industries are collaborating in developed countries on such researches. Besides, developing and underdeveloped countries stay behind in this field of research due to infirm health and e-health infrastructure, insufficient technical manpower, low physicians to patient ratio, and other limitations. Our research focuses on developing an open-source and easy to use Machine Learning Software System that should uplift Big Data and data science researches focusing on health care in the developing and underdeveloped countries amid such obstacles. This pilot study is a part of that big project that helps to make sense about the working methodology and the expected outcomes by the end of the project. Apart from medical data analysis, it could serve as an efficient platform for storing patient data and we hope academicians, professionals, and physicians around the globe will be aided by such robust data analysis software, as it facilitates automated preprocessing of data, building and comparing different prediction models, cloud storage and data visualization techniques. This work visualizes most of the part of its concept to understand its facilities, although due to some restriction some techniques will be discussed only after completing this big project.