I’m trying to create an application which predicts time to take a medicine depending on user lifestyle and medicine's restrictions.
I mean:
From a patient I take informations like:
• How many times and when does he/she eat his/her meals
• When does he/she wake up and go asleep
• How many pills does he/she have to take
From medicine's restrictions:
• Is a medicine should be eaten on empty stomach
• Is a medicine should be eaten with/without a meal
• Does a patient need to make a break inbetween meal and taking the medicine (it’s not showing on the screen below yet)
• etc
Sample dataset:
https://ibb.co/Gvry945
What type of model/mechanics/algorythm should I use to predict a time to take a medicine? Is regression the right one? I need to predict 1,2,3,4 sometimes 5 columns.
I wrote a simple code based on:
https://learn.microsoft.com/pl-pl/dotnet/machine-learning/tutorials/predict-prices
How to predict multiple labels with ML.NET using regression task?
It’s working fine and I can predict more than 1 column. But still, my problem is blank cells. When I’m trying to predict something from that data, it always shows a wrong value and it’s only working fine when all cells are complete.
So, should I spread out my dataset to few less datasets (where all cells are complete)? ex.:
https://ibb.co/m8HVPvb
When I only predict TimeToTakeMedicine1
https://ibb.co/qNk9xQL
When I predict TimeToTakeMedicine1 and TimeToTakeMedicine2
https://ibb.co/GnRc1c0
When I predict TimeToTakeMedicine1,TimeToTakeMedicine2, TimeToTakeMedicine3 and so on.
Is there any easier and better way to solve that?
Working code to predict TimeToTakeMedicine1,TimeToTakeMedicine2, TimeToTakeMedicine3 (for making it simple, I got rid of OnEmptyStomach,WithMeal and IsPossible)
using System;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Trainers;
namespace NextTry
{
class Program
{
static readonly string _trainDataPath = Path.Combine(Environment.CurrentDirectory, "DataFolder", "DataForPredictT1T2T3.csv");
static void Main(string[] args)
{
MLContext mlContext = new MLContext(seed: 0);
var model = Train(mlContext, _trainDataPath);
TestSinglePrediction(mlContext, model);
}
public static ITransformer Train(MLContext mlContext, string dataPath)
{
IDataView dataView = mlContext.Data.LoadFromTextFile<Medicine>(dataPath, hasHeader: true, separatorChar: ',');
var pipelineForMeal1 = mlContext.Transforms.CopyColumns(outputColumnName: "Label", inputColumnName: "TimeToTakeMedicine1")
.Append(mlContext.Transforms.Concatenate("Features", "MealTime1", "MealTime2", "MealTime3", "MealCount", "ActivityHoursWakeUp", "ActivityHoursSleep", "PillsCount"))
.Append(mlContext.Regression.Trainers.FastTree())
.Append(mlContext.Transforms.CopyColumns(outputColumnName: "timeToTakeMedicine1", inputColumnName: "Score"));
var pipelineForMeal2 = mlContext.Transforms.CopyColumns(outputColumnName: "Label", inputColumnName: "TimeToTakeMedicine2")
.Append(mlContext.Transforms.Concatenate("Features", "MealTime1", "MealTime2", "MealTime3", "MealCount", "ActivityHoursWakeUp", "ActivityHoursSleep", "PillsCount"))
.Append(mlContext.Regression.Trainers.FastTree())
.Append(mlContext.Transforms.CopyColumns(outputColumnName: "timeToTakeMedicine2", inputColumnName: "Score"));
var pipelineForMeal3 = mlContext.Transforms.CopyColumns(outputColumnName: "Label", inputColumnName: "TimeToTakeMedicine3")
.Append(mlContext.Transforms.Concatenate("Features", "MealTime1", "MealTime2", "MealTime3", "MealCount", "ActivityHoursWakeUp", "ActivityHoursSleep", "PillsCount"))
.Append(mlContext.Regression.Trainers.FastTree())
.Append(mlContext.Transforms.CopyColumns(outputColumnName: "timeToTakeMedicine3", inputColumnName: "Score"));
var model = pipelineForMeal1
.Append(pipelineForMeal2)
.Append(pipelineForMeal3)
.Fit(dataView);
return model;
}
private static void TestSinglePrediction(MLContext mlContext, ITransformer model)
{
var predictionFunction = mlContext.Model.CreatePredictionEngine<Medicine, MedicineTimeTakeMedicinePrediction>(model);
var medicineSample = new Medicine()
{
MealTime1 = 6,
MealTime2 = 12,
MealTime3 = 22,
MealCount = 3,
PillsCount = 3
};
var prediction = predictionFunction.Predict(medicineSample);
Console.WriteLine($"Predicted TimeToTakePill: {prediction.TimeToTakeMedicine1:0.####} ");
Console.WriteLine($"Predicted TimeToTakePill: {prediction.TimeToTakeMedicine2:0.####}");
Console.WriteLine($"Predicted TimeToTakePill: {prediction.TimeToTakeMedicine3:0.####}");
Console.ReadKey();
}
}
}
using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Data;
namespace NextTry
{
public class Medicine
{
[LoadColumn(0)]
public float MealTime1 { get; set; }
[LoadColumn(1)]
public float MealTime2 { get; set; }
[LoadColumn(2)]
public float MealTime3 { get; set; }
[LoadColumn(3)]
public float MealCount { get; set; }
[LoadColumn(4)]
public float ActivityHoursWakeUp { get; set; }
[LoadColumn(5)]
public float ActivityHoursSleep { get; set; }
[LoadColumn(6)]
public float PillsCount { get; set; }
[LoadColumn(7)]
public float TimeToTakeMedicine1 { get; set; }
[LoadColumn(8)]
public float TimeToTakeMedicine2 { get; set; }
[LoadColumn(9)]
public float TimeToTakeMedicine3 { get; set; }
}
public class MedicineTimeTakeMedicinePrediction
{
[ColumnName("timeToTakeMedicine1")]
public float TimeToTakeMedicine1 { get; set; }
[ColumnName("timeToTakeMedicine2")]
public float TimeToTakeMedicine2 { get; set; }
[ColumnName("timeToTakeMedicine3")]
public float TimeToTakeMedicine3 { get; set; }
}
}
