MACHINE LEARNING MODEL FOR MEDICAL DATA FORECASTING, Study Guides, Projects, Research of Machine Learning

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Table of Contents

• Introduction..............................................................................................................................................
• Method & Implementation.......................................................................................................................
  • Autoregressive Integrated Moving Average Model (ARIMA)............................................................
  • R Language Query...............................................................................................................................
  • Power BI Result...................................................................................................................................
  • Future Plan...........................................................................................................................................
• Conclusion...............................................................................................................................................
  • Link to final presentation.....................................................................................................................

Forecasting should be considered an art, and not science for predicting future events. Since prediction needs vary, there is seldom one superior method that works best for every organization in every given situation.

Method & Implementation

Data Source PowerBI Connector

Apply Machine Learning Method Using R (ARIMA)

Data Validation & Filtering

MA: Moving Average. A model that uses the dependency between an observation and a residual error from a moving average model applied to lagged observations.

Each of these components are explicitly specified in the model as a parameter. A standard notation is used of ARIMA (p,d,q) where the parameters are substituted with integer values to quickly indicate the specific ARIMA model being used.

The parameters of the ARIMA model are defined as follows:

p: The number of lag observations included in the model, also called the lag order.

d: The number of times that the raw observations are differenced, also called the degree of differencing.

q: The size of the moving average window, also called the order of moving average.

A linear regression model is constructed including the specified number and type of terms, and the data is prepared by a degree of differencing in order to make it stationary, i.e. to remove trend and seasonal structures that negatively affect the regression model.

A value of 0 can be used for a parameter, which indicates to not use that element of the model. This way, the ARIMA model can be configured to perform the function of an ARMA model, and even a simple AR, I, or MA model.

Adopting an ARIMA model for a time series assumes that the underlying process that generated the observations is an ARIMA process. This may seem obvious, but helps to motivate the need to confirm the assumptions of the model in the raw observations and in the residual errors of forecasts from the model.

Here is some definition:

▲ ARIMA(p,d,q) forecasting equation

▲ ARIMA(1,0,0) = first-order autoregressive model

▲ ARIMA(0,1,0) = random walk

▲ ARIMA(1,1,0) = differenced first-order autoregressive model

▲ ARIMA(0,1,1) without constant = simple exponential smoothing

▲ ARIMA(0,1,1) with constant = simple exponential smoothing with growth

▲ ARIMA(0,2,1) or (0,2,2) without constant = linear exponential smoothing

▲ ARIMA(1,1,2) with constant = damped-trend linear exponential smoothing

R Language Query

I have written the following code in the R script area; then click on Run Script.

I have to install forecast and tseries R-packages using R Shell.

library(forecast)

library(tseries)

ARIMA_Sales LTsymbol-ts(dataset$Sales_Amt,start=c(1)) Sales_Forecast LTsymbol-auto.arima(ARIMA_Sales, seasonal=TRUE) Predicted_Sales LTsymbol- forecast(Sales_Forecast, h=10) plot(Predicted_Sales)

Power BI Result

Future Plan

I am researching more for implementing the below forecasting scope