Toward a Smart Health: Reality Mining and Big Data Analytics for Real-Time Disease Prediction

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Abstract Background We are living in an age where data is everywhere and grows up in a very speedy way. Thanks to sensors, mobile phones and social networks, we can gather a hug amount of information to understand human behavior as well as his individual life. In healthcare system, big data analytics and machine learning algorithms prove their effectiveness and efficiency in saving lives and predicting new diseases. This triggered the idea of taking advantages of those tools and algorithms to create systems that involve both doctors and patients in the treatment of disease, predict outcomes and use real-time risk factors from sensors and mobile phones. Methods we distinguish three types of data: data from sensors, data from mobile phones and data registered or updated by the patient in a mobile app we created. We take advantages from IoT systems such as Raspberyy Pi to collect and process data coming from sensors. All data collected is sent to a NoSql Server to be then analyzed and processed in Databricks Spark. K-means centroid clustering algorithms is used to build the predictive model, create partitions and make predictions. To validate results in term of efficiency and effectiveness, we used clustering validations techniques: Random K, Silhouette and Elbow methods. Results The advantage of the proposed system is its capability to be applied in several prediction disease researches. As a case study, we create an e-monitoring real-time miscarriage prediction system to save baby’s lives and help pregnant women. In fact, doctors receive the results of clustering and track theirs patient through our mobile app to react in term of miscarriage to avoid non-suitable outcomes. While pregnant women receive only advices based on their behaviors. The system uses 15 real-time risk factors and our dataset contains more than 1000000 JSON files. Elbow method affirm three as the optimal number of clusters and we reach 0.99 as a value of Silhouette method, which is a good sign that clusters are well separated and matched.

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