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Table 2 Obtained final selection of features using the Boruta algorithm and a voting system (presence of the feature in at least 3 out of the 5 sets)

From: Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome

Description

Type

Height (cm)

Numeric

Presence of cognitive impairment

Binary

Presence of depression

Binary

Mobility Scale follow-up question (tiredness when going out)

Binary

Mobility Scale question (stair-climbing ability)

Binary

Mobility Scale follow-up question (tiredness when walking outside)

Binary

Mobility Scale question (walking outside ability)

Binary

MMSE follow-up question (remembering objects ability)

Categorical

Total GDS

Binary

Age in years

Numeric

ADL question (difficulty washing)

Categorical

Number of ADL abilities

Numeric

Number of IADL abilities

Numeric

IADL question (difficulty using telephone)

Categorical

IADL question (difficulty shopping)

Categorical

IADL question (difficulty cooking)

Categorical

IADL question (difficulty doing light housework)

Categorical

IADL question (difficulty doing heavy housework)

Categorical

IADL question (difficulty using public transportation)

Categorical

Total MMSE score

Numeric

Sum of mobility score main features (em1,em2, em3,em4,em5)

Numeric

Number of drugs (drug intake)

Numeric

Alkaline phosphatase [U/L]

Numeric

Presence of polypharmacy

Binary

Self-reported health status

Categorical

Self-reported health status compared to people the same age

Categorical

Capacity of dealing with problems

Categorical

Capacity of dealing with tasks

Categorical

GDS question (dropped activity of interests)

Binary

GDS question (boredom)

Binary

Presence of joint inflammation (more than 4 weeks in a row)

Categorical