Research Paper

Taiga Akiyama

Professor Kein

HUM 102 L64

March 29, 2016

Machine Learning and Medical Diagnosis: Affordable Medical Care

It is estimated that there are currently over thirteen million children under the age of five that die from illnesses that could have been easily treated (“Right to Healthcare around the Globe). Many areas in the world lack the resources or money. How can machine learning be implemented in an automated program to diagnose illnesses in areas that lack medical foundation and resources? Machine learning used in conjunction with medical diagnosis could help these areas have a jump start in medical knowledge and resource. Creating a program that automatically diagnoses patients would decrease the amount of money needed to support areas, increase the medical technology, and create a better connection between engineers and medical professionals.

This program will be targeted for users that are in areas that lack resources. These resources include funds to build any sort of medical facility, or areas that lack any sort of technologies to aide themselves with. This program will be designed so that it can be implemented at a low cost to provide affordable medical diagnosis to patients. However, this program can also be implemented in countries that already have a stable medical foundation. It can be used by casual patients who don’t feel the need to physically visit the doctor, but are somewhat worried about their health.

Currently, in order to aid countries or areas, we need to spend a lot of resources to get doctors across the globe, in working order, with some sort of medical facility. For example, Doctors Without Borders shows that they use more than eighty five percent of their donations to fund program activities (Donation FAQ, 2013). Expenses include emergency and medical programs, program support and development, field staff, and communications (Donation FAQ, 2013). In 2010, the revenue of the group, “all raised through private contributors, such as individuals, foundations, and corporations” totaled to two hundred and sixty four million dollars (Donation FAQ, 2013). Simple calculation would give us that the Doctors Without Borders spends approximately two hundred and twenty four million dollars in their programs. Using artificial intelligence and machine learning AI, it will be possible to create a means of medical diagnosis that is cheaper to fund and to implement in countries.

Why choose an automated program over a medical professional? How would a machine learning AI be proficient in diagnosing patients? The biggest benefit of a machine learning program is that it can easily handle large data sets with ease. This means that the program will be able to math up user data with existing data to efficiently compile a set of answers. In Leung’s article it is mentioned that one of the principle uses of machine learning is predicting phenotypes from biomarkers such as the genome (Leung, Section II). Their programming follows a powerful approach where the computational model is trained to predict measurable intermediate cell variables (Leung, Section II).

The program will be operated based on two main users: the patient and an administrator. The patient enters information about them self, and receive a diagnosis. The administrator would be in charge of defining new diseases, finding an appropriate database of training sets, and training the system (Kampouraki, 469). The program can be based on Support Vector Machines (SVMs), for the foundation of the algorithm. SVMs are supervised learning models that analyze data and proceed to decisions, based on their knowledge (Kampouraki, 467).

One of the biggest challenges that must be overcome is the relationship and connection between software engineers and medical professionals. There is a stigma against engineers by doctors that do not believe that an automated program would ever be reliable enough to replace a physical doctor. Although this proposal is not meant to completely replace a physical doctor, the question of reliability still stands. Can an algorithm reliably predict a possible disease that a patient may have?  A performance test done by Manickapriya and Nimala seems to suggest that they were able to validate the accuracy and efficient of active learning leading to reliable and authentic predictions (Manickapriya & Nimala, 2636). They state that clustering data sets are useful, but it is hard to determine the value of some preference “p” or how to measure similarity between user-submitted data and existing data sets (Manickapriya & Nimala, 2636).

A machine learning AI used to create an automated medical diagnosis would close the gap between many areas. It would allow for the acceleration of medical knowledge in technology in areas that lack a basic medical foundation or facility, would allow for cheap implementation across the world, and would build a stronger connection between engineers and doctors that would allow for future enhancements in medicinal technology.

Works Cited

"Donation FAQ." MSF USA. N.p., 18 Nov. 2013. Web. 15 Mar. 2016.

Kampouraki, A., Vassis, D., Belsis, P., & Skourlas, C. (2013). e-Doctor: A Web based Support Vector Machine for Automatic Medical Diagnosis. Procedia - Social And Behavioral Sciences, 73(Proceedings of the 2nd International Conference on Integrated Information (IC-ININFO 2012), Budapest, Hungary, August 30 - September 3, 2012), 467-474. doi:10.1016/j.sbspro.2013.02.078

Kononenko, I. (2001). Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in medicine, 23(1), 89-109.

Leung, M. K., Delong, A., Alipanahi, B., & Frey, B. J. (2015). Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets. Proceedings Of The IEEE In Press, 4 December 2015, doi:10.1109/JPROC.2015.2494198

Manickapriya, S., & Nimala, K. (2015). Machine learning approach for medical diagnosis. ARPN Journal Of Engineering And Applied Sciences, 10(6), 2634-2637.

"Right to Healthcare around the Globe." Humanium - Together for Children's Rights. Humanium, n.d. Web.

Son, L. H., & Thong, N. T. (2015). Intuitionistic fuzzy recommender systems: An effective tool for medical diagnosis. Knowledge-Based Systems, 74, 133-150. doi:10.1016/j.knosys.2014.11.012 Web. 8 Mar. 2016. (Not yet used)

Tomczak, J. M., & Zięba, M. (2015). Probabilistic combination of classification rules and its application to medical diagnosis. Machine Learning, 101(1-3), 105-135. doi:10.1007/s10994-015-5508-x Web. 8 Mar. 2016. (Not yet used)