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Handbook on Intelligent Healthcare Analytics


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       • Why were the patients infected with a particular disease last month?

       • Why were the patients in the emergency ward last month?

      Predictive analytics: Predictive analytics uses machine learning and artificial Intelligence to analyze the present and the past data. This type of analytics is used to predict and forecast the future. The predictive analytics use descriptive data to forecast what will happen in the future possibly. This predictive analysis is the very significant analytics for predicting and diagnosing the disease. These types of analysis have capability for controlling and avoiding non communicable diseases like cancer, heart disease, stroke, and diabetes. Noncommunicable diseases are together accountable for most of the deaths worldwide. The predictive analytics use patient history and patient current health information to make medical decisions. The predictive analytics identify the following:

       • Identify the patients who have the maximum possibility of getting diabetics, stroke, and heart attack?

       • Identify the patients who have the maximum risk of hospitalization?

      The above questions are answered using the data and machine learning for forecasting. In predictive analytics, the researchers and data analysts train a model using data mining algorithms, machine learning techniques, artificial intelligence, and the deep learning algorithms to predict the future events.

      Prescriptive analytics: This analytics makes recommendations from the predicted output. The prescriptive analytics in healthcare is the last course of action in analyzing medical big data. This analysis has the capacity to suggest the action to overcome the problem in the healthcare organization. The imminent opportunity and challenges of medical big data is prescriptive analytics. Prescriptive analytics is the most advanced level of data analytics in medical big data. It is going to be realistic in the near future due to machine learning techniques, data science, cloud computing, deep learning, data engineering, and artificial intelligence.

      These analytics give the recommendation or suggest action to change the prediction. Prescriptive analytics uses the data, the business intelligence for insight, and the machine learning for forecasting.

      Big data analytics perform the above analysis using machine learning, artificial intelligence, and natural language processing to explore the unknown patterns and relationships among data.

      Healthcare sector is the top most sector which generates large volumes of data. So, big data is having a huge impact in the healthcare industry. The EHR of patients is widely adopted and analyzed to get deeper insights on clinical knowledge and for enhanced knowledge about illness and disease. The healthcare organization uses big data that improves the efficiency of healthcare practice and care. Big data with the recent development of data mining techniques, machine learning algorithms, deep learning, artificial intelligence, and image processing used to find many important features. The major applications of big data in the healthcare industry are discussed in the following sections.

      3.5.1 Real Time Healthcare Monitoring and Altering

      Remote patient monitoring or telehealth is healthcare service for patients and doctors, which use IoT devices to track and analyze health status [25].

      3.5.2 Early Disease Prediction with Big Data

Patient health checking devices Features
Glucose checking meter It used by diabetes patients for checking the glucose level.
Heart rate monitoring It is used to monitor heart rate.
Hand hygiene monitoring It is used in hospital to remind the people to sanitize their hands before entering hospital room.
Depression monitoring It collects information like heart rate and BP and analyzes this information for a patient᾽s mental health.
Blood pressure (BP) monitoring This device monitors blood pressure. Some BP devices take multiple readings for the daily averages.
Pulse oximeter This device is used to track the oxygen saturation level in blood and also the pulse rate of the patient.
Patient wearable Patient wearable devices are used to sense heart rate, blood pressure, glucose levels, weight, and physical activities.
Maternity care monitoring This remote monitoring reduces clinic visits for pregnant women.
Electrocardiography (ECG) devices This device is used to diagnose cardiac abnormalities.

      Predictive analysis mainly used by hospitals:

       • To accurate diagnosis of disease

       • To reduce healthcare costs

       • For preventive medicine

      Predictive analyses with medical data are used for determining the risk level of patients for disease based on lifestyle choices and health history.