A Systematic Review of Applications of Machine Learning in Cancer Prediction and Diagnosis

Abstract

Advancement in genome sequencing technology has empowered researchers to think beyond their imagination. Researchers are trying their hard to fight against various genetic diseases such as cancer. Artificial intelligence has empowered research in the healthcare sector. The availability of open-source healthcare datasets has motivated the researchers to develop applications which helps in early diagnosis and prognosis of diseases. Further, Next-generation sequencing has helped to look into detailed intricacies of biological systems. It has provided an efficient and cost-effective approach with higher accuracy. The advent of microRNAs also known as small noncoding genes has begun the paradigm shift in oncological research. We are now able to profile expression profiles of RNAs using RNA-seq data. microRNA profiling has helped in uncovering their relationship in various genetic and biological processes. Here in this paper, we present a review of the machine learning perspective in cancer research. The best way to develop effective cancer treatment/drugs is to better understand the intricacies and complexities involved in the cancer microenvironment. Although there has been a plethora of methods and techniques proposed in the literature, still the deadliness of cancer can't be reduced. In such a situation Artificial intelligence (AI) or machine learning is providing a reliable, fast, and efficient way to deal with such stringent diseases.