A system for identifying susceptibility to developing chronic kidney disease for a feline, the system comprising: a processor; and a memory that stores code that, when executed by the processor, causes the computer system to: receive at least one input level of one or more biomarkers from the feline and optionally an input level of an age of the feline, wherein at least one of the one or more biomarkers comprises information relating to a urine specific gravity level, a creatinine level, a urine protein level, a blood urea nitrogen or urea level, a white blood cell count, urine pH, or any combination thereof; analyze and transform the at least one input level of the one or more biomarkers and optionally the input level of the age by organizing and/or modifying each input level to derive a probability score or a classification label via a classification algorithm, wherein the classification algorithm comprises code developed from a training dataset, the training dataset comprising medical information relating to both a first plurality of biomarkers and optionally ages from a first set of sample felines and a second plurality of biomarkers and optionally ages from a second set of sample felines, wherein the classification algorithm is developed using a training algorithm; wherein the classification algorithm is one of a hard classifier, which determines the classification label of whether the feline is at risk of developing CKD, or a soft classifier, which determines the probability score of the feline developing CKD; generate an output, wherein the output is the classification label or the probability score; determine or categorize, based on the output, whether the feline is at risk of developing CKD; and determine a customized recommendation based on the determining or categorizing. a processor; and a memory that stores code that, when executed by the processor, causes the computer system to: receive at least one input level of one or more biomarkers from the feline and optionally an input level of an age of the feline, wherein at least one of the one or more biomarkers comprises information relating to a urine specific gravity level, a creatinine level, a urine protein level, a blood urea nitrogen or urea level, a white blood cell count, urine pH, or any combination thereof; analyze and transform the at least one input level of the one or more biomarkers and optionally the input level of the age by organizing and/or modifying each input level to derive a probability score or a classification label via a classification algorithm, wherein the classification algorithm comprises code developed from a training dataset, the training dataset comprising medical information relating to both a first plurality of biomarkers and optionally ages from a first set of sample felines and a second plurality of biomarkers and optionally ages from a second set of sample felines, wherein the classification algorithm is developed using a training algorithm; wherein the classification algorithm is one of a hard classifier, which determines the classification label of whether the feline is at risk of developing CKD, or a soft classifier, which determines the probability score of the feline developing CKD; generate an output, wherein the output is the classification label or the probability score; determine or categorize, based on the output, whether the feline is at risk of developing CKD; and determine a customized recommendation based on the determining or categorizing. receive at least one input level of one or more biomarkers from the feline and optionally an input level of an age of the feline, wherein at least one of the one or more biomarkers comprises information relating to a urine specific gravity level, a creatinine level, a urine protein level, a blood urea nitrogen or urea level, a white blood cell count, urine pH, or any combination thereof; analyze and transform the at least one input level of the one or more biomarkers and optionally the input level of the age by organizing and/or modifying each input level to derive a probability score or a classification label via a classification algorithm, wherein the classification algorithm comprises code developed from a training dataset, the training dataset comprising medical information relating to both a first plurality of biomarkers and optionally ages from a first set of sample felines and a second plurality of biomarkers and optionally ages from a second set of sample felines, wherein the classification algorithm is developed using a training algorithm; wherein the classification algorithm is one of a hard classifier, which determines the classification label of whether the feline is at risk of developing CKD, or a soft classifier, which determines the probability score of the feline developing CKD; generate an output, wherein the output is the classification label or the probability score; determine or categorize, based on the output, whether the feline is at risk of developing CKD; and determine a customized recommendation based on the determining or categorizing.