Enhanced hybrid algorithm for cancer detection in medical big data analytics

Abstract

The impact of Machine Learning (ML) algorithms and data mining techniques in Big Data analysis is proved to be significant across different domains. The data that are available online is very large and complex and are expected to get doubled in the size every two years if the current trend of data accumulation continues. The researchers identified that the data in the healthcare are projected to raise with an annual growth rate (AGR) of as high as 36% by the year 2025, than in other domains such as manufacturing, visual media and in financial sectors according to the report of International Data Corporation (IDC). The need to reduce the healthcare costs and the use of big data are the driving the growth of the healthcare Artificial Intelligent market. However, reluctance by medical professionals to adopt AI-based technologies, lack of a skilled workforce, and unclear regulatory guidelines for medical software are among the factors restraining healthcare in AI. Cancer is identified to be one of the fatal diseases and chief cause for deaths in human over the past decade. The diseases when identified in an early stage are more likely to be cured successfully. Almost 80% of the affected patients are expected to survive at least for a year if the diagnosis is done in an early stage which is very high when compared with 15% of survival when the same diagnosis is done in a later stage of the disease. Only 20% of consumers would trust AI-generated advice for healthcare, according to the survey. For the successful deployment of AI, there is a need for deployment, integration, support, and maintenance services. There is a dramatic research opportunity in increasing the scalability, efficiency and accuracy of the AI systems designed for medical diagnosis newline