India
Abstract
In the current generation, it is very important to monitor our health. With the busy lives of people nowadays, many are experiencing health-related issues at an early age. Many of these issues arise because of our daily life activities. People are interested in many activities, but they hardly know the consequences of those activities. Hence it is very important to detect daily life activities that affect the health of a person and predict the diseases that may come in the future. However, there are existing methods for predicting a particular kind of disease like diabetes, tuberculosis, etc., based on electronic health records. The proposed system predicts the overall health status of a person using machine learning techniques. The overall health status includes how well a person is sleeping, eating, doing physical activity, etc. Also, the proposed system monitors the health of persons and alerts when they are deviating from a normal state. In this chapter, we will discuss the data collection approach, architecture of the system, overall health estimation models, implementation details, and the analysis of the result.
Keywords: Healthcare data analysis, machine learning in healthcare, data analytics, health status estimation
2.1 Introduction
2.1.1 Health Status of an Individual
The overall health status of a person is assessed by comparing the level of wellness with the level of illness. The health status can be estimated through many parameters. Some of the parameters are (i) Sleep status: the health level of a person is depending on his/her sleep timings, (ii) Screen status: the health level of a person is depending on the amount of time spent on screen, (iii) Drink status: the health level of a person is depending on his/her drinking habits, (iv) Smoke status: the health level of a person is depending on his/her smoking activities (v) Calories status: the health level of a person is depending on the calories consumed and physical activities.
2.1.2 Activities and Measures of an Individual
The things that an individual does daily can be referred to as activities. Some of the activities include sleeping, watching television, consuming alcohol, smoking cigarette, listening to the radio, reading books, etc. Measures of an Individual include physical measures like height, weight, and some other measures like age, gender, etc. Basically, many of the measures are permanent they will not change frequently, whereas the activities might change frequently.
2.1.3 Traditional Approach to Predict Health Status
In general, health status can be predicted by consultancy experts. If an individual wants to know about their sleep status (i.e. whether their sleep pattern is good? And whether they are taking the adequate amount of sleep?), they can consult an expert at sleep centers. If an individual wants to know about their calorie status (i.e. How much calories they need to consume to maintain/increase/decrease weight? How much exercise they need to do to maintain the calories in balance?), they can consult physicians.
But what the experts do, they give some suggestions by considering the measures and activities mentioned previously. For this, the experts use some rules and conditions on the measures and activities. For example, ‘A boy of age 21, height 176 cm, weight 63 kg with a less physical activity needs to consume 1,950 calories per day to maintain weight’. But the limitation of this approach is not considered some of the important parameters like (i) Different Health Parameters (Sleep Status, Calorie Status, etc.) have different consultants. (ii) They may not be very accurate in predicting them manually without any calculations. (iii) Consulting experts might be costly for a low-middle and middle-class family.
Thus, it demands the need for designing a model that can predict their health status from daily life activities.
2.2 Background
It is important for everyone to understand their health status, it helps to avoid future diseases. As mentioned previously some of the parameters of the health status are sleep status, smoke status, drink status, disease status, etc. Directly or indirectly they depend on the individual’s daily life activities and physical measures. In healthcare data management, a huge amount of structured or unstructured data related to the patient is generated from the diagnostic reports, doctor’s prescription, and the wearable devices. In recent years the healthcare data analysis and estimating the future health status are the major focused domains in healthcare. Disease Prediction has a major impact on healthcare analytics as it predicts outbreaks of epidemics to avoidable diseases and improves the quality of life. Some of the recent works proposed a verity of models to predict health status a person with the help of various factors. Researchers Sahoo, Mohapatra, and Wu [10], proposed a cloud-based probabilistic data acquisition method and also, designed an approach to predict the impending health state of a person based on the current health status. A work by Hirshkowitz et al. [5], proposed a method to evaluate and recommended sleep duration for individuals based on their age categories. Researchers [9], proposed a new approach for the disease risk prediction, in that they also proposed the Convolutional Neural Network (CNN) based on unimodal disease risk prediction and CNN-based Multimodal Disease Risk Prediction. Reseachers Weng, Huang, and Han [2], discussed different types of artificial neural network (ANN) techniques for disease prediction and evaluated all the methods based on statistical tests. Researchers [7], proposed a system to collect health data through some questionnaires and analyzed using deep learning architectures.
A work by Tayeb et al. [12], proposed a method based on the popular machine learning algorithm KNN to predict heart disease and chronic kidney failure. Researchers [6] proposed an automated system for the prediction of stroke based on Electronic Medical Claims (EMCs), and they compared the Deep Neural Network (DNN) with the gradient boosting decision tree (GBDT), logistic regression (LR) and support vector machine (SVM) approach. Researchers [8] proposed the cloud-based smart clothing system for sustainable monitoring of human health. They also discussed the technologies and the implementation of methodologies. Reseachers Schmidt, Tittlbach, Bös & Woll [11], analyzed varieties of physical activity, fitness and health, they considered 18 years duration for study and identified interesting insights. In a recent work on Analyzing University Fitness Center data [14], the user’s fitness activity data is collected to predict the crowd at the fitness center. But the fitness activity data can be used to predict more than that.
A lot of research was done on measuring health parameters numerically. Also, there are many works on calculating some health parameters from other parameters. A work by Harris-Benedict [4] calculates Basal Metabolic Rate from an individual’s physical measures. It is used to estimate the number of calories needed for an individual to maintain good health. Our work incorporated the effect of daily life activities on health status. But that data can be used to personalize health predictions and suggestions. This motivated to design a model that predicts health status from the daily life activities of individuals.
2.3 Problem Statement
Let At be the set of daily life activities done by an individual t day’s back. Thus, A0 is the set of activities done by an individual today, A1 be the set of activities done by an individual yesterday, and so on. A is the collection of the activities of an individual for many days. M be the set of physical measures of an individual. H be the health status matrix.
Definition 1: Health Status Matrix: A health status matrix M describes the outcome of various parameters of health status. Each row of the matrix is considered as a vector of possible outcomes of the respective parameter of the health status. Examples of health status parameters are sleep status, smoke status, drink status, etc.
Given a set of daily life activities
