Cancer　is　a　kind　of　non-communicable　disease,　progresses　with　uncontrolled　cell　growth　in　the　body.　The　cancerous　cell　forms　a　tumor　that　impairs　the　immune　system,　causes　other　biological　changes　to　malfunction.　The　most　common　kinds　of　cancer　are　breast,　prostate,　leukemia,　lung,　and　colon　cancer.　The　presence　of　the　disease　is　identified　with　the　proper　diagnosis.　Many　screening　procedures　are　suggested　to　find　the　presence　of　the　condition　under　different　stages.　Medical　practitioners　further　analyze　these　electronic　health　records　to　diagnose　and　treat　the　individual.　In　some　cases,　misdiagnosis　can　happen　due　to　manual　error　or　misinterpretation　of　the　data.　To　avoid　these　issues,　this　paper　presents　an　effective　computer-aided　diagnosis　system　supported　by　intelligence　learning　models.　A　machine　learning-based　feature　modeling　is　proposed　to　improve　predictive　performance.　From　the　University　of　California,　Irvine　repository,　breast,　cervical,　and　lung　cancer　datasets　are　accessed　to　conduct　this　experimental　study.　Supervised　learning　algorithms　are　employed　to　train　and　validate　the　optimal　features　reduced　by　the　proposed　system.　Using　the　10-Fold　cross-validation　method,　the　trained　and　performance　model　is　evaluated　with　validation　metrics　such　as　accuracy,　f-score,　precision,　and　recall.　The　study＇s　outcome　attained　99.62%,　96.88%,　and　98.21%　accuracy　on　breast,　cervical,　and　lung　cancer　datasets,　respectively,　which　exhibits　the　proposed　system＇s　efficacy.　Moreover,　this　system　acts　as　a　miscellaneous　tool　for　capturing　the　pattern　from　many　clinical　trials　for　multiple　types　of　cancer　disease.　©　2021　Elsevier　Ltd