A Disease Diagnosis and Treatment Recommendation System Based on Big Data Mining and Cloud Computing

TL;DR

This paper presents a cloud-based disease diagnosis and treatment recommendation system that leverages big data mining techniques, including clustering and association rules, to improve accuracy and accessibility in medical decision-making.

Contribution

It introduces a novel DDTRS integrating DPCA and Apriori algorithms with parallel processing on Apache Spark for enhanced disease diagnosis and treatment recommendations.

Findings

Effective disease-symptom clustering achieved

Accurate disease treatment recommendations generated

System demonstrates high performance and low latency

Abstract

It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages. However, most classification methods might be ineffective in accurately classifying a disease that holds the characteristics of multiple treatment stages, various symptoms, and multi-pathogenesis. Moreover, there are limited exchanges and cooperative actions in disease diagnoses and treatments between different departments and hospitals. Thus, when new diseases occur with atypical symptoms, inexperienced doctors might have difficulty in identifying them promptly and accurately. Therefore, to maximize the utilization of the advanced medical technology of developed hospitals and the rich medical knowledge of experienced doctors, a Disease Diagnosis and Treatment Recommendation System (DDTRS) is proposed in this paper. First, to effectively identify disease symptoms more accurately, a Density-Peaked Clustering Analysis (DPCA) algorithm is introduced for disease-symptom clustering. In addition, association analyses on Disease-Diagnosis (D-D) rules and Disease-Treatment (D-T) rules are conducted by the Apriori algorithm separately. The appropriate diagnosis and treatment schemes are recommended for patients and inexperienced doctors, even if they are in a limited therapeutic environment. Moreover, to reach the goals of high performance and low latency response, we implement a parallel solution for DDTRS using the Apache Spark cloud platform. Extensive experimental results demonstrate that the proposed DDTRS realizes disease-symptom clustering effectively and derives disease treatment recommendations intelligently and accurately.