Arkaprabha Sau|Ishita Bhakta
Predicting anxiety and depression in elderly patients using machine learning technology
healthcare technology lettersvolume 4 issue 6 p 238243 articleopen access predicting anxiety depression elderly patients using machine learning technology arkaprabha sau corresponding author arkaprabha sau arkadoctorgmailcom orcidorg0000000323073272 department community medicine rg kar medical college hospital kolkata 700 004 indiasearch papers authorishita bhakta ishita bhakta department computer science engineering west bengal university technology kolkata 700064 indiasearch papers author arkaprabha sau corresponding author arkaprabha sau arkadoctorgmailcom orcidorg0000000323073272 department community medicine rg kar medical college hospital kolkata 700 004 indiasearch papers authorishita bhakta ishita bhakta department computer science engineering west bengal university technology kolkata 700064 indiasearch papers author first published 10 november 2017 httpsdoiorg101049htl20160096citations 42aboutsectionspdf toolsrequest permissionexport citationadd favoritestrack citation 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sociodemographic healthrelated factors using machine learning technology ten classifiers evaluated data set 510 geriatric patients tested tenfold crossvalidation method highest prediction accuracy 89 obtained random forest rf classifier rf model tested another data set separate 110 older patients external validity predictive accuracy found 91 false positive fp rate 10 compared gold standard tool 1 introduction population ageing globally 1 developed developing countries world experiencing growth absolute number geriatric population 1 worldwide 607 million people elderly age group year 2000 increased 901 million 2015 projected united nations grow 14 billion 2030 21 billion year 2050 1 terms proportion elderly population total population 2000 10 increased 12 2015 shown fig 1 2 india proportion elderly population 77 2000 increased 89 2015 shown fig 2 2 3 fig 1open figure viewerpowerpoint total population elderly population absolute number million proportion worldwide fig 2open figure viewerpowerpoint total population elderly population absolute number million proportion india according world health organization healthy person possess healthy mind along physical fitness 3 changes mental health thought processes one important agerelated changes among elderly people throughout world anxiety depression two important mental health disorders associated 4 vulnerability associated consequences anxiety depression also increase age progresses 5 often found elderly patients presented physician surgeon suffering multiple somatic symptoms dyspepsia fatigue headache body ache joint pain truly manifestations underlying anxiety depression 6 important morbid conditions hypertension htn irritable bowel syndrome chronic obstructive pulmonary disease asthma diabetes obesity coronary artery diseases significantly associated anxiety depression among elderly patients 69 even anxiety depression associated increased risk death among patients suffering coronary heart disease 10 11 suicidal tendencies significantly higher among depressed elderlies 12 estimates globally 712 47 older population suffering depression anxiety respectively 5 systematic review metaanalysis articles estimated prevalence anxiety depression ranges 7 49 various countries world 13 india one country high prevalence anxiety depression mental health disorders among contributes almost 12 global burden disease projected increased 20 2030 literatures suggest prevalence anxiety depression ranges 10 48 elderly population different places country 13 14 considering worldwide increasing trend anxiety depression mental diseases announces world health day theme year 2017 depression lets talk give special emphasis problems 15 anxiety depression happen elderly individual time although preventable treatable yet difficult diagnose proper time general physicians gps due lack obvious symptoms among elderlies 12 16 often diagnosis made several years onset moreover proper diagnosis anxiety depression appropriate management made psychiatrists psychologists owing scarcity trained manpower especially developing countries india difficult diagnose treat anxiety depression among elderly patient proper time minimise morbidity mortality associated valid reliable psychometric analysis tools screen elderly individual suffering anxiety depression 30 15 items geriatric depression scale hamilton anxiety depression scale hospital anxiety depression scale hads etc 1618 used general practitioners also however problem takes least 530 min complete tools busy outpatient department opd hours hospital clinic developing countries practically possible gp specialist administer tool doctor population ratio also unfavourable compared developed countries even indoor pds often seems undoable task due high patient load result proper diagnosis management initiates late stage disorder contributes higher incidence morbidity mortality associated automated disease prediction system older patients suffering anxiety depression required help lot gp specialists fields specialisation screen patient underlying diseases system also refer concerned specialist appropriate diagnosis management early stage help improve overall mental health status country consequences associated also minimised globally almost 66 disability adjusted life year loss elderly people averted 5 various sociodemographic factors disease conditions play vital role development anxiety depression elderly patients age sex marital status socioeconomic conditions family environment literacy job security past history depression chronic medical conditions strongly associated anxiety depression among older people 1921 factors used predictors development automated anxiety depression prediction system predictor variables put automated system one necessarily medical paramedical person including patient based prediction made within seconds 2 materials methods obtaining ethical clearance institutional ethics committee rg kar medical college hospital kolkata west bengal india data collected 520 geriatric patients attended general opd hospital january august 2016 evaluated anxiety depression investigator using hads classified two different classes ie anxiety andor depression present anxiety depression absent 22 feature selection approaches applied reduce feature dimension various classifiers evaluated selected features using machine learning technology waikato environment knowledge analysis weka version 380 httpwwwcswaikatoacnzmlwekadocumentationhtml system default values various parameters applied classifier training testing done using tenfold crossvalidation method classifier highest predictive accuracy selected external validation purpose data selected features collected another 110 geriatric patients attended general opd hospital september october 2016 prediction made time 110 geriatric patients also classified two abovementioned classes using hads finally confusion matrix generated predictive model respect hads classification accuracy calculated confusion matrix methodology depicted fig 3 followed concise description step fig 3open figure viewerpowerpoint flow diagram methodology 21 data collection rg kar medical college hospital kolkata one important governmentoperated tertiary care institution eastern india general opd operated monday saturday gp informed consent taken every patient data collection data age sex literacy residence marital status recent bereavement employment status socioeconomic status personal income pi past history anxiety past history depression family history depression kind addiction pain multiple sites diabetes htn hearing problem visual impairment mobility impairment insomnia collected investigator 520 geriatric patients attended opd various problems features selected extensive literature review consulting psychiatrists hospital features represented different probable causative dimensions anxiety andor depressions among geriatrics personal attributes age sex sociodemographic attributes literacy residence marital status recent bereavement economic conditions employment status socioeconomic status pi past medical history past history anxiety depression family history depression kind addiction current medical conditions pain multiple sites diabetes htn hearing problem visual impairment mobility impairment insomnia table 1 statuses anxiety depression evaluated using hads table 1 methodology nutshell step involved description data collectionfeatures age sex literacy residence marital status recent bereavement employment status socioeconomic status pi past history anxiety depression family history depression kind addiction pain multiple sites diabetes htn hearing problem visual impairment mobility impairment insomnia collected 520 geriatric patients time also evaluated anxiety depression using hads scale characterised two groups ie anxiety andor depression present anxiety depression absent feature selection weka selected evaluators weka cfs subset gr pc su features selected attribute evaluators listed table 2 classification selected attributesfeatures ten classifiers bn nb log mlp smo ks rs j48 rf rt chosen predict anxiety depression elderly patients statistics classifier mentioned tables 3 4 external validation data selected features collected another 110 geriatric patients predictions anxiety depression made selected classifier time 110 geriatric patients also classified using hads confusion matrix evaluated two classification systems accuracies machine learning classifier obtained considering classification made hads gold standard 22 hospital ads psychometric analysis scale administered physicians screening anxiety andor depression among patients simultaneously originally constructed zigmond snaith 1983 22 consists 14 questions seven evaluate anxiety seven evaluate depression responses scored 0 3 ordinal scale patient score depending response 0 21 either anxiety depression systematic review metaanalysis large number published articles identified cutoff point 8 21 anxiety depression 23 scale cutoff point valid reliable geriatric population levels health care 24 questionnaire average 5 min required identify person suffering anxiety andor depression using score hads every geriatric patient classified initially one four categories ie anxietyno depression anxiety depression anxietydepression later four outcomes grouped two outcome variables ie anxiety andor depression present anxiety depression absent groupings made aim screening geriatric patients anxiety andor depression referred psychiatrist psychologists appropriate management probably suffering either anxiety depression 23 features selection attribute evaluation algorithms initially 20 features predict outcome table 1 owing effect collinearity interaction within features predictive accuracy learning algorithm may suffer find redundant features minimise feature dimension five different attribute evaluators applied filter approach finally best fitted set features identified applied subsequent predictive modelling following section contains brief description attribute evaluators correlation feature selection cfs subset evaluator 25 evaluates merit subset attributes considering individual predictive capability attribute along degree redundancy subsets features highly correlated class low collinearity preferred one r 26 evaluates worth attribute using classifier generates simple rules feature select rule minimal error principal component analysis pca 27 reduces features dimensions choosing enough eigenvectors account percentage variance original data gain ratio gr symmetrical uncertainty su 28 evaluators based measurement entropy system measures gr systems uncertainty respect class respectively 24 classification algorithms binary class classification problem different machine learning algorithms handle kind healthrelated classification issues efficiently 29 ten different classifiers employed evaluated select optimal one selected set features bayesian network bn logistic multiple layer perceptron mlp nave bayes nb random forest rf random tree rt j48 sequential minimal optimisation smo random subspace rs k star ks ten different classifiers represent different principles applied machine learning bn nb based bayesian principle logistic based regression principle mlp follows feed forward artificial neural network modelling rt j48 decision treebased classifiers whereas rf ensemble learning method smo used training support vector machine svm ks instancebased classifier uses entropybased distance function rs meta classifier based bagging bn 30 classifier represents data form nodes directed acyclic graph relationship nodes follows conditional probabilistic principles network trained based relationship among nodes whole network logistic classification log 31 applied outcome variables categorical attribute variable feature contribution prediction expected outcome data set predictive capability attribute measured using maximumlikelihood estimation statistics logistic model calculates probability prediction binary outcome using input data set model uses likelihood ratio wald test test statistical significance mlp 32 basic simple artificial neural network used supervised learning network composed set inputs single output output depends weighted sum input nodes obtain desired result complex data mlp required input weighted sum previous layer fed next layer final output unit activate input crosses threshold target output achieved computations stopped number hidden layers nodes layer depend applications nb classifier 28 supervised learning method based probabilistic attitude attributes specific class probability calculated using nb algorithm rt classifier constructs tree directed acyclic graph based stochastic process considers k randomly chosen attributes node option allow estimation class probabilities rf 33 assembly random decision tree classifier prediction output rf classifier made prediction outputs individual classifier base learner used rf rt classifier selects features randomly help bagging concept rs 34 meta classifier used weka uses reduced error pruning tree default base classifier method constructs decision treebased classifier maintains highest accuracy training data improves generalisation accuracy grows complexity classifier consists multiple trees constructed systematically pseudorandomly selecting subsets components feature vector trees constructed randomly chosen subspaces final classification result decided simple majority voting rule smo provides simple optimised training algorithm svm usually solve complex problems quadratic programming optimisation applied learning svm makes computation procedures slower smo provides easy way problem divides large problem smaller ones solves separately less memory requirement 35 smo handle large set data memory requirement linear quadratic j48 28 classifier uses c45 decision tree algorithm treebased classification splits input data different trees based range values attribute set ks 36 instancebased classifier means class test instance based class training instances similar similarity test training sets determined similarity function ks differs instancebased learners uses entropybased distance function measure similarity 3 results among 510 geriatric patients 281 55 males 229 45 females mean standard deviation age 685 485 years among 482 suffering kind mental health problems either anxiety depression 518 patients free mental health issues reduce feature dimension various attribute evaluations applied result reported table 2 table 2 comparison five different attribute evaluator algorithms order attribute evaluatorfeature selection algorithms cfs best fit pca gr su search method ranker 1 2 p 3 e r e p 4 e r e r r 5 r e 6 p p htn 7 h 8 htn htn v v pi 9 h h h htn 10 v v htn pi v nb age diabetes mellitus e employment status h hearing impairment htn hypertension marital status p pain multiple sites pi personal income r recent bereavement sex v visual impairment comparing various feature selection algorithms table 2 20 initial features 11 identified important age sex marital status employment status pi pain multiple sites recent bereavement diabetes mellitus htn visual impairment hearing impairment information pi reliable feature especially indian scenario often hesitate tell true figure due sociocultural beliefs norms removed selected feature set finally ten features selected next stage ten classifiers evaluated selected set features confusion matrix generated tenfold crossvalidation reported table 3 table 3 confusion matrix ten classifiers classifier classified b kappa bn 184 62 05944 b 41 223 nb 223 23 05949 b 81 183 log 200 46 04501 b 95 169 mlp 193 53 05567 b 60 204 smo 231 15 05118 b 111 153 ks 185 61 05055 b 65 199 rs 219 27 07491 b 37 227 j48 220 26 07569 b 36 228 rf 221 25 07803 b 31 233 rt 206 40 07014 b 36 228 nb anxiety andor depression present b anxiety depression absent furthermore classifiers compared terms accuracy accu true positive tp rate fp rate precisionpositive predictive value ppv fmeasures f area receiver operating characteristic roc curve auc summarised table 4 depicted fig 4 table 4 evaluation classifiers terms different metrics classifier evaluation metrics accu tp fp ppv f auc bn 798 798 155 818 797 889 nb 796 796 196 814 794 853 log 724 724 27 734 722 811 mlp 778 778 221 779 778 85 smo 753 753 234 797 746 759 ks 753 753 247 753 753 814 rs 875 875 124 875 875 917 j48 878 878 12 879 878 86 rf 89 89 109 891 89 943 rt 851 851 15 851 851 85 fig 4open figure viewerpowerpoint bar diagram showing accuracy fp rate auc ten different classifiers clear table 4 fig 4 rf appropriate classifier data set highest accuracy 89 tp rate 89 precisionppv 891 fmeasure 89 auc 943 lowest fp rate 109 also 31 external validation test data set containing information ten selected features collected another 110 elderly patients attended general opd hospital september october 2016 simultaneously levels anxiety depression assessed using hads questionnaire result recorded comparison next stage result considered gold standard data set prepared contained ten selected features missing decision class first previously built rf classifier model loaded weka test data set supplied rf model reevaluated test data set class prediction made instance result recorded comparison gold standard result confusion matrix table 5 following metrics calculated reported table 6 table 5 confusion matrix prediction machine learning technology rf versus gold standard hads result test data set rf gold standard test hads total b 47 6 53 b 4 53 57 total 51 59 110 nb anxiety andor depression present b anxiety depression absent table 6 evaluation rf classifier test data set metrics performance accuracy 91 tp rate 92 fp rate 10 precision 89 specificity 90 fscore 90 diagnostic odds ratio 104 4 discussion machine learning technology new promise development automated disease diagnostic system researchers medical engineering fields trying address issue together letter endeavour made predict one important agerelated mental changes ie anxiety depression among older people sociodemographic medical factors using machine learning technology considerably large number 510 elderly patients interviewed screened anxiety depression data set used predictive modelling study conducted suhasini et al 37 tried predict mental health problems 400 psychiatric patients exclusively older patients using machine learning technology however kind study specially focusing elderly patient hardly available literature study conducted bhakta sau 38 prediction depression among elderlies living slum kolkata found among four different classifiers bn logistic mlp smo bn smo algorithms predicted depression accuracy almost 90 letter first relevant features selected using attribute evaluator weka ten features found effective ten machine learning classifiers evaluated rf highest predictive accuracy tenfold crossvalidation test rf model tested another 110 elderly patients external validity predictive accuracy found 91 fp rate 10 compared gold standard hads letter pave way future research work search better classifier appropriate features especially suitable geriatric patients moreover extended identify older persons suffering underlying mental health problems general population community level 5 conclusion letter illustrates promise machine learning technology field automated disease prediction system prediction model based small number sample size hardly generalisable multicentric research large data set comparing different classifiers appropriate use optimisation techniques help build generalised predictive model lead develop efficient effective automated system predict anxiety depression among elderly patient within seconds help physicians surgeons address different healthrelated issues proper emphasis mental health status older patients 6 funding declaration interests conflict 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