Big Medical Data Decision-Making Intelligent System Exploiting Fuzzy Inference Logic for Prostate Cancer in Developing Countries

In most developing countries, it has become a severe challenge for the limited medical resources and outdated healthcare technology to meet the high demand of large population. From the perspective of social development, this unbalanced healthcare system in developing counties has also exacerbated the contradiction between physicians and patients, particularly those suffering from malignant diseases (such as prostate cancer). Rapid improvements in artificial intelligence, computing power, parallel operation, and data storage management have contributed significantly to a credible medical data decision-making on the detection, diagnosis, treatment, and prognosis of malignant diseases. Consequently, to address these existing problems in the current healthcare field of developing countries, this paper proposes a novel big medical data decision-making model exploiting fuzzy inference logic for prostate cancer in developing countries, constructing an intelligent medical system for disease detection, medical data analysis and fusion, treatment recommendations, and risk management. Based on 1 933 535 items of hospitalization information from over 8000 prostate cancer cases in China, the experimental results demonstrate that the intelligent medical system could be adopted to assist physicians and medical specialists in coming up with a more dependable diagnosis scheme.