Actually this project has been done by my senior, and he used neural network to predict both target. So now I has to use other method to predict the targets. I have done some research regarding predictive modelling.
https://en.wikipedia.org/wiki/Predictive_modelling
And also other sources that may be related :
https://www.quora.com/What-are-some-Machine-Learning-algorithms-that-you-should-always-have-a-strong-understanding-of-and-why
http://www.tutorialspoint.com/data_mining/dm_classification_prediction.htm
http://rayli.net/blog/data/top-10-data-mining-algorithms-in-plain-english/
So based on the research that I've been done, like in the Quora link given above, Sean Owen encouraged to use Random Forest for classification/regression. Also other method that catch my attention is Naive Bayes.
Based on the data that have been given to me by my supervisor (Shamimi A. Halim), so I started to play it with my weka tools.
Four method I used in this research :
1) Multilayer Perceptron (Backpropogation)
2) Naive Bayes
3) Random Forest
4) Logistic Regression
Multilayer Perceptron
Want to learn more :
https://en.wikipedia.org/wiki/Multilayer_perceptron
For this try-n-error research, the data have been preprocessed and just focus on one output which is status(Alive or Dead). Data set for training is 90%, the other 10% for testing. Total data is 204.
Parameter :
Result :
From the above result, I only got 70% accuracy.
So after try other parameters, I got the best(maybe?) parameter which have 3 hidden layers.
Parameter :
Result :
From the result I got 85% accuracy.
Naive Bayes
Want to learn more :
https://en.wikipedia.org/wiki/Naive_Bayes_classifier
No parameter.
Result :
From the result I got 75% accuracy.
Random Forest
Want to learn more :
http://www.listendata.com/2014/11/random-forest-with-r.html
https://en.wikipedia.org/wiki/Random_forest
Parameter :
Result :
From the above result, I only got 70% accuracy.
So after try other parameters, I got the best(maybe?) parameter which numFeatures(number of features) set to 6 and (I dont know the function of seed, but I think it is related to randomness)seed set to 5.
Parameter :
Result :
From the image above, we got 85% accuracy which is same with Multi Layer Perceptron.
Logistic Regression
So why I choose Logistic Regression to try-n-error? Based on the definition in wikipedia :
In statistics, logistic regression, or logit regression, or logit model[1] is a regression model where the dependent variable (DV) is categorical.
Based on my output, which are Alive or Dead, it is categorical. That's why I try this method too.
Parameter :
Result :
Yeah! 85% accuracy. Same with Multi Layer Perceptron and Random Forest result.
So what now? I dont know. Lol. Maybe I have to study the algorithms before decide which method suitable and efficient for the data.