Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

Abstract

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Keywords: cancer; carcinoma; computational oncology; machine learning; mathematical modeling; telemedicine; tumor.

Figure 1

Percentage of newly diagnosed cases and deaths by areas worldwide in 2020. (A) Both male and female, (B) male, and (C) female [5].

Figure 2

History behind the development of MMs in the field of biomedical studies [22].

Figure 3

Simplified illustration for working objective of NN method of ML [16].

Figure 4

An example of the curse of dimensionality. (a). Outcome of multiple discontinuities (b). Outcome of continuous sensor computation [77].

Figure 5

General working process of a classifiers [88].

Figure 6

History of MM in the field of cancer research.

Figure 7

Classifications of ML [108].

Figure 8

Applications of ML and MM in new drug discovery [39].

Figure 9

The applications of ML algorithms in lung cancer management [258].

Figure 10

Commonly used ML classifiers in colon cancer [266].

Figure 11

Schematic representation of achieving worthwhile therapeutic outcomes using a combination of ML and MM techniques in cancer treatment [351].

Figure 12

Challenges of ML application and cyber threats in healthcare.