Simple　Summary　Breast　cancer　is　one　of　the　foremost　causes　of　cancer-related　mortality　in　women.　It　is　curable　and　controllable　only　if　detected　early.　Microcalcifications　in　breast　tissue　are　essential　predictors　for　radiologists　to　detect　early-stage　breast　cancer.　This　study　proposes　a　method　for　detecting　and　classifying　microcalcifications　in　mammogram　images　to　predict　breast　lesions,　using　machine　learning　coupled　with　an　interpretable　radiomics　approach.　The　method　was　evaluated　using　a　publicly　accessible　dataset,　which　may　aid　radiologists　and　clinicians　in　identifying　breast　cancer　in　their　regular　clinical　practices.　This　study　contributes　to　the　field　of　predictive　modeling　in　healthcare.　Microcalcifications　in　breast　tissue　can　be　an　early　sign　of　breast　cancer,　and　play　a　crucial　role　in　breast　cancer　screening.　This　study　proposes　a　radiomics　approach　based　on　advanced　machine　learning　algorithms　for　diagnosing　pathological　microcalcifications　in　mammogram　images　and　provides　radiologists　with　a　valuable　decision　support　system　(in　regard　to　diagnosing　patients).　An　adaptive　enhancement　method　based　on　the　contourlet　transform　is　proposed　to　enhance　microcalcifications　and　effectively　suppress　background　and　noise.　Textural　and　statistical　features　are　extracted　from　each　wavelet　layer＇s　high-frequency　coefficients　to　detect　microcalcification　regions.　The　top-hat　morphological　operator　and　wavelet　transform　segment　microcalcifications,　implying　their　exact　locations.　Finally,　the　proposed　radiomic　fusion　algorithm　is　employed　to　classify　the　selected　features　into　benign　and　malignant.　The　proposed　model＇s　diagnostic　performance　was　evaluated　on　the　MIAS　dataset　and　compared　with　traditional　machine　learning　models,　such　as　the　support　vector　machine,　K-nearest　neighbor,　and　random　forest,　using　different　evaluation　parameters.　Our　proposed　approach　outperformed　existing　models　in　diagnosing　microcalcification　by　achieving　an　0.90　area　under　the　curve,　0.98　sensitivity,　and　0.98　accuracy.　The　experimental　findings　concur　with　expert　observations,　indicating　that　the　proposed　approach　is　most　effective　and　practical　for　early　diagnosing　breast　microcalcifications,　substantially　improving　the　work　efficiency　of　physicians.