Abstract
Abstract: The identification of ia health issue, disease, illness, or other condition that a person imay haveiis iknown ias ia disease idiagnosis.Sometimesiit may be extremely simple to diagnoseiaidisease,butiotheritimesiit may be more difficult. Large data sets iareiaccessible,butithe number ofitools i that can reliably iidentify ipatterns iand iforecast ioutcomes iis constrained. iTraditional imethods for diagnosing idiseases iarei labour-intensive iand iprone ito inaccuracy.When comparedito solely ihumanicompetence, ithe use of iAI iprediction iapproaches enables auto diagnosis and reduces detection mistakes. iFor ithe ipast ten years,i from January 2009 to December 2019, we have examined the recent literatureiin this publication.iA itotal iof i105 article iwere idiscoveredi in ithe ieight imost popular idatabases ithat iwere itaken into consideration by ithe iinvestigation. iThe imosti populariAIitechniquesiforimedicalidiagnosticisystems iwereicategorised after a thorough review of those papers. iWe ialso goioverinumerousidiseases and the irelevant iAI methods, such as Fuzzy Logic, Machine Learning,iand Deep Learning. iThe goaliof this study iarticle iis ito provide isome ikey iinsights iabout ithe various AI technique now being applied in ithe imedical industry,iparticularly in the areas iof heart disease iprediction, brain disease,iprostate, iliver and kidneyiillness.Last ibut not least, the report resolved issues
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