Frequency domain analysis of time-resolved fluorescence measurements (TRFM) is an extremely rapid technique for medical diagnostics thanks to its unique sensitivity to a wide variety of physical and chemical features. Nevertheless, the determination of the underlying fluorescence lifetime (FLT) data of samples by their frequency response data (FRD), demands fitting algorithms. Therefore, the interpretation of the precise changes in the FLT of complex environments in term of biochemical processes is a challenge as it involves uncertainties associated with the chosen fitting algorithm. This research suggests a novel characterization procedure based on the squared distance (D2) between the FRD of the samples that avoid the inherent blurring caused by the transformation of the FRD into FLT data. The D2 approach was validated through simulated data of 6 classes with similar FLT characteristics, where the accuracy of D2 classification was about 96%. In addition, this approach was tested on experimental FRD from 43 individual samples that their preliminary physician diagnosis divided them into 4 groups: 5 healthy samples served as controls, 9 samples diagnosed with diverse types of bacteria, 16 samples diagnosed with diverse types of viruses and 13 samples were negatives to any bacterial or viral infection, although presenting related symptoms. Using the D2 analysis, the classification of 28/30 matched the physician diagnosis and the classification of 41/43 samples matched earlier report. In conclusion, this work demonstrated that the D2 model can aid in disease identification and increase the specificity and sensitivity of conventional medical procedures or TRFM-based diagnosis.