Electronic　health　records　are　used　to　extract　patient＇s　information　instantly　and　remotely,　which　can　help　to　keep　track　of　patients＇　due　dates　for　checkups,　immunizations,　and　to　monitor　health　performance.　The　Health　Insurance　Portability　and　Accountability　Act　(HIPAA)　in　the　USA　protects　the　patient　data　confidentiality,　but　it　can　be　used　if　data　is　re-identified　using　＇HIPAA　Safe　Harbor＇　technique.　Usually,　this　re-identification　is　performed　manually,　which　is　very　laborious　and　time　captivating　exertion.　Various　techniques　have　been　proposed　for　automatic　extraction　of　useful　information,　and　accurate　diagnosis　of　diseases.　Most　of　these　methods　are　based　on　Machine　Learning　and　Deep　Learning　Methods,　while　the　auxiliary　diagnosis　is　performed　using　Rule-based　methods.　This　review　focuses　on　recently　published　papers,　which　are　categorized　into　Rule-Based　Methods,　Machine　Learning　(ML)　Methods,　and　Deep　Learning　(DL)　Methods.　Particularly,　ML　methods　are　further　categorized　into　Support　Vector　Machine　Methods　(SVM),　Bayes　Methods,　and　Decision　Tree　Methods　(DT).　DL　methods　are　decomposed　into　Convolutional　Neural　Networks　(CNN),　Recurrent　Neural　Networks　(RNN),　Deep　Belief　Network　(DBN)　and　Autoencoders　(AE)　methods.　The　objective　of　this　survey　paper　is　to　highlight　both　the　strong　and　weak　points　of　various　proposed　techniques　in　the　disease　diagnosis.　Moreover,　we　present　advantage,　disadvantage,　focused　disease,　dataset　employed,　and　publication　year　of　each　category.