Predictive Analysis - Health Risk Assessment Using Deep Learning


In this blog we will discuss Health risk assessment using Deep Learning Algorithms.


What is Predictive analysis ?


Predictive analytics helps connect the data to effective action by drawing a reliable conclusion which a data analyst can predict the future based on the current and previous data. This term is mainly used for analytics and statistical techniques.


Health Risk Assessment Deep Learning


The healthcare sector is lacking in actionable knowledge. This industry faces challenges in essential areas like electronic record management, data integration, and computer-aided diagnoses and disease predictions. It needs to reduce the healthcare cost and healthcare movements. Rapidly expanding the field of predictive analytics and deep learning play a pivotal role in the evolution of large volumes of healthcare data research. Deep learning provides a wide range of techniques, tools and framework to address these challenges. Nowadays Health data is expanding rapidly in various formats. This health data offers more opportunities for health data analysis and enhancement of health services by innovative approaches.


Predictive analytics helps healthcare life sciences and providers and applies many techniques from statistics, data mining, modeling, machine learning, and artificial intelligence to investigate current findings to make predictions about the future. It helps healthcare organizations to prepare for health care by optimizing the cost, diagnosing the diseases accurately, enhancement of patient care, resource optimization and improves clinical outcomes


The concept of deep learning is to dig a large volume of data to automatically identify patterns and extract features from complex unsupervised data without the involvement of humans, which makes it an important tool in big data analysis. Deep learning plays an important role in diagnostic applications. Deep learning techniques can reveal clinically relevant information hidden in the large data with a guidance of relevant clinical questions to assist clinical decision-making and in turn provides the physicians the analysis of any disease accurately for better treatment, thus resulting in better medical decisions.


Predictive analytics using Deep Learning


Health risk assessment predictive analytics aims to predict the health related outcomes based on clinical or non clinical patterns in the data.


There are two methods to build the predictive model


First, The collection of patient data in clinical trials with a set of predefined protocols. For example, Lung cancer risk prediction model, UK prospective diabetes study (UKPDS), Heart disease Prediction etc.


Second, The use of existing patient data collected in clinical practice, such as EHRs, insurance claims, and clinical registries. For instance, the inpatient mortality predictive model.


Predictive models capture the characteristics of the specific event. The UKPDS risk engine can predict coronary heart disease and stroke in patients with type 2 diabetes.


Deep learning is widely used for medical imaging analysis in several different application domains. Medical imaging techniques such as MRI scans, CT scans, ECG, used to diagnose dreadful diseases such as heart disease, cancer, brain tumor. Hence, with the help of deep learning, the doctors can analyze the disease better and provide patients with the best treatment. In addition, deep learning is used to analyze medical insurance fraud claims. Moreover, deep learning helps the insurance industry to send out discounts and offers to their target patients. Deep learning technique used to detect Alzheimer’s disease at an early stage in which the medical industry faces the challenges currently. Deep learning techniques are used to understand a genome and help patients get an idea about the disease that might affect them, which has a promising future also.


Deep Learning Framework


Deep learning combines advances in computing power and neural networks with many layers to learn complicated patterns in large amounts of data. It is an extension of the classical neural network and uses more hidden layers so that the algorithms can handle complex data with various structures.



Deep learning collects a large volume of data, including patients records, medical reports, and insurance records, and applies its neural networks to provide the best outcomes. Therefore, it is important to involve a deep learning role to resolve healthcare issues due to its representational and recognition supremacy that assists healthcare personnel to determine, predict, analyze, and practice its theories for the delivery of healthcare.


Deep Learning Models


Feature engineering is the main difference between traditional machine learning algorithms and deep learning algorithms. It required domain expertise and a time consuming process. Deep learning involves automatic feature engineering.



Traditional machine learning algorithm


Deep learning algorithm

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