Predictive Analysis — Health Risk Assessment Using Deep Learning

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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

Convolutional neural network (CNN) model is the most commonly used deep learning algorithm. CNNs are composed of neurons that have learnable weights and biases. Each neuron receives inputs and creates a dot product. The complete network expresses a single differentiated function that scores the input aligned data of health attributes in accordance with the classes of health risks. CNNs have been proven to be more efficient in training inputs with a restricted number of parameters and hidden units. CNN can achieve local connections and tied weights efficiently by pooling translation invariant features. This specialty is adaptive to our design, as the input health data has been normalized and the output health risks have been predefined in certain classes

We developed a diabetes Risk Prediction model Using Deep learning Algorithms. This Work is used to predict the diabetes in a patient. The dataset used here is the Pima indians diabetes. The dataset consists of 768 entries having 9 features.

  • Pregnancies — Number of times pregnant
  • Glucose — Plasma glucose glucose concentration a 2 hours in an oral glucose tolerance test
  • Blood Pressure — Diastolic Blood Pressure (mm hg)
  • Skin Thickness — Triceps skinfold thickness (mm)
  • Insulin — 2 Hours serum insulin (mu U/ml)
  • BMI — Body mass index (weight in kg/(height in m)²
  • Diabetes Pedigree Function — Diabetes Pedigree Function
  • Age — Age (years)
  • Outcomes — class variable (0 or 1) 268 of 768 are 1 the others are 0(1 means the patient is diabetic and 0 means the patient is non diabetic)

Sample Of Dataset :

Here we can see the Training and validation accuracy and training and validation loss of diabetic Risk assessment model.



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