1. A method for the treatment of ovarian cancer in an individual, the method comprising: (I) providing a dataset comprising concentrations of a plurality of lipids in a sample from said individual, and (II) applying a classification model according to one of (A)-(C) as follows to generate a transformed dataset: Classification Model A: (i) a 340×340 transformation matrix as shown in Appendix B1 and an SVM model as shown in Appendix C1; (ii) a 340×85 transformation matrix comprising the first 85 columns of the matrix as shown in Appendix B1 and an SVM model as shown in Appendix C4; (iii) a 340×10 transformation matrix comprising the first 10 columns of the matrix as shown in Appendix B1 and an SVM model as shown in Appendix C7; (iv) a 82×82 transformation matrix as shown in Appendix B4 and an SVM model as shown in Appendix C10; or (v) a 77×77 transformation matrix as shown in Appendix B7 and an SVM model as shown in Appendix C13; Classification Model B: (i) a 340×340 transformation matrix as shown in Appendix B2 and an SVM model as shown in Appendix C2; (ii) a 340×87 transformation matrix comprising the first 87 columns of the matrix as shown in Appendix B2 and an SVM model as shown in Appendix C5; (iii) a 340×29 transformation matrix comprising the first 29 columns of the matrix as shown in Appendix B2 and an SVM model as shown in Appendix C8; (iv) a 82×82 transformation matrix as shown in Appendix B5 and an SVM model as shown in Appendix C11; or (v) a 77×77 transformation matrix as shown in Appendix B8 and an SVM model as shown in Appendix C14; or Classification Model C: (i) a 340×340 transformation matrix as shown in Appendix B3 and an SVM model as shown in Appendix C3; (ii) a 340×44 transformation matrix comprising the first 44 columns of the matrix as shown in Appendix B3 and an SVM model as shown in Appendix C6; (iii) a 340×9 transformation matrix comprising the first 9 columns of the matrix as shown in Appendix B3 and an SVM model as shown in Appendix C9; (iv) a 82×82 transformation matrix as shown in Appendix B6 and an SVM model as shown in Appendix C12; or (v) a 77×77 transformation matrix as shown in Appendix B9 and an SVM model as shown in Appendix C 15; and (III) subjecting the transformed dataset of step (II) to SVM analysis with an SVM model of the classification model, in which: a) when a classification model according to Classification Model A is used, an output of >0 indicates a normal sample, and an output of <0 indicates an ovarian cancer sample; b) when a classification model according to Classification Model B is used, an output of >0 indicates a benign sample, and an output of <0 indicates a malignant sample; and c) when a classification model according to Classification Model C is used, an output of >0 indicates an early stage ovarian cancer sample, and an output of <0 indicates a late stage ovarian cancer sample; whereby a status of the sample is determined, and wherein, when said output <0 indicates an ovarian cancer sample, a malignant sample, or a late-stage ovarian cancer sample, a treatment is administered comprising administering a therapeutic agent selected from the group consisting of carboplatin, paclitaxel, topotecan, liposomal doxorubicin, gemcitabine, cisplatin, and a combination of carboplatin and paclitaxel.