Algorithm 5. Heart disease prediction using Gaussian Naïve Bayes.

Input: EHR Data

Output: Boolean Status

$asyncpredictHeartDisease(patientData)\{$

$constpredictorVariables[]=\{patientData.age,patientData.sex,patientData.cp,patientData.trestbps,patientData.chol,patientData.fbs,patientData.restecg,patientData.thalach,patientData.exang,patientData.oldpeak,patientData.slope,patientData.ca,patientData.thal\};$

$const{X}_{Train},Y_{Train}$ = $train\mathrm{\_}test\mathrm{\_}split(data,analyzevalue,proximatevalue)$

let Ytrain = $X_{train}.map((item)=>item["target"]);$

$let{Y}_{test}=X_{test}.map((item)=>item["target"]);$

$varmo{d}_{X\mathrm{\_}Train}=[]$, $mo{d}_{X\mathrm{\_}Test}=[];$

$C_{XTrain}.forEach((key)=>\{xTra.push(X_{train[i][key])};\});$

$mo{d}_{X\mathrm{\_}Train}.push(xTra);$

$model=newGuassianNB()$

$model.train(X_{Train},Y_{train});$

$letprediction=model.predict(mo{d}_{X\mathrm{\_}Test});$

$result=prediction$;

return result;

}