Analyzing the Impact of Machine Learning on Cancer Treatments

1.1 Significance of Research

Technological advancement in healthcare institutions has gained momentum in current phenomena where the entire world is revolving around modern tools—for example, using smart applications to connect with various people, opting for home delivery, online shopping, remote diagnosis, online treatment henceforth. Therefore, it becomes essential for experts of the healthcare sector to apply modern technologies while performing several activities like recording patients' history and medical practitioners enforced to reform treatment procedures. Hence, this overall subject will help understand the important role of machine learning in cancer treatment using R. In fact, R is known as chemotherapy or R-CHOP, frequently given in cycles three weeks apart. Every cycle of R-CHOP lasts for almost three weeks, completely based on size and kind of tumor (Zhang et al., 2017). The treatment is generally incurred in a chemotherapy per day unit. It means that the entire process is a complicated one, in which machine learning helps in processing various complex concepts or terms. Thus, this research will explore techniques to perform cancer analysis and outline cancer treatment processing. We can present how experts use machine learning in understanding complicated concepts or images of diseases.

1.2 Research Aim and Questions

Cancer is a very dangerous disease for the human body that destroys the functioning of a person's body organs completely. As a result, practitioners of the healthcare sector are opting for several advanced technologies to facilitate cancer patients with creative services or ideas. For example, machine learning aided doctors in identifying cancer symptoms at a prior stage, which aids in curing it as soon as possible (Koutsouleris et al., 2018). Therefore, conducting an in-depth investigation on cancer treatment alternatives is a smart move in this innovative environment. Hence, the foremost aim of this research is “to analyze the impact of machine learning on cancer treatment by using R”. In order to collect further information on this subject, several related questions are demonstrated as follows.

• Ø What is the role of machine learning in the healthcare sector?

• Ø What is the significance of machine learning in cancer treatment with the use of R?

1.3 Research Problem

According to the designed title, this research revolves around machine learning how this technology has reformed medical services and covers examples of cancer treatment to understand the crucial role of technology. However, the foremost problem of research is to describe the role of machine learning in treating cancer with the help of suitable examples. Cancer is a life-taking disease that is not easy to recover from. Experts of the healthcare sector are trying to conduct an in-depth investigation to identify accurate solutions to cancer. Therefore, it will be very problematic to outline the role of machine learning in cancer treatment success via chemotherapy (Redlich et al., 2016).

This research investigated the relationship between machine learning techniques with the health sector, especially cancer treatment, and then conducted cancer prediction using several machine learning algorithms, including Random Forest, Naïve Bayes, and Decision Tree. The results show that all of the three algorithms achieved accuracy over 80%, and Random Forest achieved the best result, which is 96.47%. In addition, a computer with machine learning algorithms is far more efficient than human beings, capable of performing thousands of biopsies in just mini-seconds. Thus, the utilization of machine learning algorithms can largely reduce the workload of medical experts and make cancer treatment more efficient.