Machine Learning Pipeline Multiple Sclerosis PRO ...

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Using Patients Reported Outcomes (PRO), Clinical Scales (CS) and ... FOR MULTIPLE SCLEROSIS COURSE DETECTION ... Life Satisfaction Index. OAB. PRO.
A MACHINE LEARNING PIPELINE 
 FOR MULTIPLE SCLEROSIS COURSE DETECTION
 FROM CLINICAL SCALES AND PATIENT REPORTED OUTCOMES Samuele Fiorini1, Alessandro Verri1, Andrea Tacchino2, Michela Ponzio2, Giampaolo Brichetto2, Annalisa Barla1 
 1- DIBRIS - Università degli Studi di Genova, Italy
 2- AISM - Scientific Research Area, Italian MS Foundation, Genova, Italy [email protected], {alessandro.verri, annalisa.barla}@unige.it, {andrea.tacchino, michela.ponzio, giampaolo.brichetto}@aism.it

Machine Learning Pipeline Min-Max Scaling

Data

Principal Components Analysis Linear Discriminant Analysis

2

kY

Problem Setting

2

Nested list of features

wXk2 + µ kwk2 + ⌧ kwk1 Y

˜ w ˜X

2 2

+

RLS LR SVM

2

kwk ˜ 2

`1 `2 Feature Selection

Data Exploration

2

kY wXk22 + kwk2 2 Logit(Y, fw ) + kwk2 2 Hinge(Y, fw ) + kwk2 KNN

Best Model

Linear Classification fw (x) = wT x

Multiple Sclerosis PRO and CS data understanding Using Patients Reported Outcomes (PRO), Clinical Scales (CS) and anthropometric measures the aim is to learn a statistical model for the classification of MS courses by means of machine learning techniques. The proposed classifier is based only on a meaningful subset of the available features. Dataset Description

Data Exploration

Name

Type

#Items Description

MS Course

#Patients

AGEo

measure

1

Age at the onset of the disease

Relapsing Remitting

RR

170

AGEd

measure

1

Age at the disease diagnosis

Secondary Progressive

SP

205

AGEv

measure

1

Age at the examination

Primary Progressive

PP

68

W

measure

1

Weight [Kg]

Progressive Relapsing

PR

8

H

measure

1

Height [cm]

Benign

B

6

MFIS

PRO

21

Modified Fatigue Impact Scale

HADS

PRO

14

Hospital Anxiety and Depression Scale

LIFE

PRO

11

Life Satisfaction Index

OAB

PRO

8

Overactive Bladder Questionnaire

FIM

CS

19

Functional Independence Measure

MOCA

CS

11

Montreal Cognitive Assessment

PASATT

CS

1

Paced Auditory Serial Addition Task

SDMT

CS

1

Symbol Digit Modality Test

In the study, we considered PRO and CS for 457 patients represented by 91 features.

PCA

LDA

The data exploration outcome is a binary classification setting: RR vs. ALL

ℓ1ℓ2 Feature Selection

Linear Classification

The outcome of ℓ1ℓ2 FS is a set of nested lists of relevant features with increasing level of correlation.

A set of linear models is tested in the RR vs. ALL classification scenario. We recall that, since the two classes are not balanced, the accuracy score of a random classifier is around 62.8%. Our experiments shows that the ℓ1ℓ2 feature selection can significantly improve the performance of the considered linear classifiers.

For optimal values of λ and 𝜏, the parameter µ governs the amount of correlation included in the model.

In the table below OLS can be considered as a particular case of RLS with λ = 0.

For µ1, the smallest value of µ, ℓ1ℓ2 FS provides a list of discriminant features ranked according to their selection frequency, i.e. how many times each feature was selected in a double cross-validation procedure.

Name

The best features from the sparsest model (µ = µ1) are used as prototypes in a centroid-based clustering procedure. The Pearson correlation was used as similarity measure. The features on the list associated with maximum value of µ = µ8 were considered. The outcome is the identification of groups of maximally correlated features.

Selection Description Frequency

LIFE 004

100%

These are the best years of my life

AGEv

100%

Age at the examination

FIM 011

100%

Type of transfer: tub or shower

FIM 012

100%

Locomotion: walking

FIM 014

100%

Locomotion: stairs

LIFE 009

88%

I would not change my past life even if I could

MFIS 020

62%

I have limited my physical activities

LIFE 005

25%

Most of the things I do are boring or monotonous

MFIS 014

25%

I have been physically unconfortable

HADS 007

25%

I can sit at ease and feel relaxed

OLS and RLS are the algorithms that benefit more from the ℓ1ℓ2 feature selection step. %

Accuracy =

OLS

T P +T N T P +F P +T N +F N Accuracy

Precision = Recall = F1 = 2 ·

TP T P +F P

Precision·Recall Precision+Recall

F1 Score

KNN

LR

SVM

NO FS

71,79 72,42 72,44 77,28

75,69

ℓ1ℓ2 FS

78,32 78,24 74,99 77,30

75,82

OLS

TP T P +F N

RLS

RLS

KNN

LR

SVM

NO FS

0,618 0,623 0,620 0,652

0,634

ℓ1ℓ2 FS

0,701 0,702 0,666 0,623

0,670

References
 L1L2Signature - http://slipguru.disi.unige.it/Software/L1L2Signature/ LIFE 004

AGEv FIM 011

C.DeMol, S.Mosci, M.Traskine, and A.Verri, “A regularized method for selecting nested groups of relevant genes from microarray data”. Journal of Computational Biology, vol. 16, no. 5, pp. 677– 690, 2009. A. Barla, S. Mosci, L. Rosasco, and A. Verri, “A method for robust variable selection with significance assessment”. ESANN, 2008, pp. 83–88. F. D. Lublin and S. C. Reingold, “Defining the clinical course of multiple sclerosis results of an international survey”. Neurology, vol. 46, no. 4, pp. 907–911, 1996.

FIM 012 FIM 014

C. Granger, A. Cotter, B. Hamilton, R. Fiedler, and M. Hens, “Functional assessment scales: a study of persons with multiple sclerosis”. Archives of physical medicine and rehabilitation, vol. 71, no. 11, pp. 870–875, 1990.